UPSI Digital Repository (UDRep)
|
UPSI Digital Repository (UDRep)
|
|
|
| ||||||||||||||||||||
| Abstract : Universiti Pendidikan Sultan Idris |
| The aim of this study is to design and develop a systematic dataset for multiple skin tones and to
analyse the reasons behind misclassifications of skin and non-skin, using different deep learning
models, colour spaces, and different optimisation parameters. Related academic literature have
cited three problems, namely data-related issues (e.g. skin-like), data volume (e.g. large volume
requires high computer source), and technical issues (e.g. optimising parameters). Two
articles on Deep Learning (DL) for skin detection failed to address the issues extensively.
DL foundation is a training dataset and the quality of training depends on the quality of
the data-input. To address the issues, a systematic dataset consisting of 17 million patches
was created for multiple skin tones with (skin-like) images. The dataset was then converted into
different colour spaces with multiple labels that characterise different scenarios, running
different DL. Experimentally utilised YCbCr and CNN present high performance of binary
and multi-class classifications. Binary classification of skin and skin-like resulted in 98% and
multi-class classification of four classes 84% and 69% for five classes respectively. Furthermore,
a binary classification between skin tone and skin-like (e.g. black skin tone and black
skin-like) resulted in 97%, 81%, 60%, and 51% for black, brown, medium, and fair
consequently. From empirical experiment, darker skin tone is a better classification accuracy
followed by optimising parameters (Hidden-Layers, Neurons, Activations-Functions, Optimiser,
Initialiser, Data-Input, and Data-Size). A hybrid CNN-RNN benchmark improves the accuracy by
99% compared with 98%, and 97% for SAE compared to 91% as reported. By studying different skin
scenarios, one can analyse the reasons behind overlapping between skin and skin-tones.
This is a promising study for further research by developing and applying a generalised version
of skin detector with different applications.
|
| References |
Abdullah-Al-Wadud, M., & Chae, O. (2008). Skin segmentation using color distance map and water-flow property. Paper presented at the Information Assurance and Security, 2008. ISIAS'08. Fourth International Conference on.
Ahmadi, E., Garmsirian, F., & Azimifar, Z. (2012). A discriminative fusion framework for skin detection. Paper presented at the Artificial Intelligence and Signal Processing (AISP), 2012 16th CSI International Symposium on.
Aibinu, A. M., Shafie, A. A., & Salami, M. J. E. (2012). Performance analysis of ANN based YCbCr skin detection algorithm. Procedia Engineering, 41, 1183-1189.
Al-Mohair, H. K., Mohamad-Saleh, J., & Suandi, S. A. (2014). Color space selection for human skin detection using color-texture features and neural networks. Paper presented at the Computer and Information Sciences (ICCOINS), 2014 International Conference on.
Al-Mohair, H. K., Saleh, J. M., & Suandi, S. A. (2015). Hybrid human skin detection using neural network and K-means clustering technique. Applied Soft Computing, 33, 337-347.
Al-Tairi, Z. H., Rahmat, R. W. O., Saripan, M. I., & Sulaiman, P. S. (2014). Skin Segmentation Using YUV and RGB Color Spaces. JIPS, 10(2), 283-299.
Al Abbadi, N. K., Dahir, N. S., Al-Dhalimi, M. A., & Restom, H. (2010). Psoriasis Detection Using Skin Color and Texture Features 1.
Araban, S., Farokhi, F., & Kangarloo, K. (2011a). Determining effective colour components for skin detection using a clustered neural network. Paper presented at the 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).
Araban, S., Farokhi, F., & Kangarloo, K. (2011b). Determining effective colour components for skin detection using a clustered neural network. Paper presented at the Signal and Image Processing Applications (ICSIPA), 2011 IEEE International Conference on.
Arya, S., Pratap, N., & Bhatia, K. (2015). Future of face recognition: A review. Procedia Computer Science, 58, 578-585.
Aznaveh, M. M., Mirzaei, H., Roshan, E., & Saraee, M. (2008). A new and improves skin detection method using RGB vector space. Paper presented at the Systems, Signals and Devices, 2008. IEEE SSD 2008. 5th International Multi-Conference on.
Aznaveh, M. M., Mirzaei, H., Roshan, E., & Saraee, M. (2008). A new color based method for skin detection using RGB vector space. Paper presented at the Human System Interactions, 2008 Conference on.
Bilal, S., Akmeliawati, R., Salami, M. J. E., & Shafie, A. A. (2015). Dynamic approach for real-time skin detection. Journal of Real-Time Image Processing, 10(2), 371-385.
Bouguila, N. (2011). Bayesian hybrid generative discriminative learning based on finite Liouville mixture models. Pattern Recognition, 44(6), 1183-1200.
Bouirouga, H., El Fkihi, S., Jilbab, A., & Bakrim, M. h. (2010). A comparison of skin detection techniques for objectionable videos. Paper presented at the I/V Communications and Mobile Network (ISVC), 2010 5th International Symposium on.
Bouirouga, H., Lrit, S. E., Jilbab, A., & Aboutajdine, D. (2011). Recognition of adult video by combining skin detection features with motion information. Paper presented at the Multimedia Computing and Systems (ICMCS), 2011 International Conference on.
Brancati, N., De Pietro, G., Frucci, M., & Gallo, L. (2017). Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering. Computer Vision and Image Understanding, 155, 33- 42.
Bush, I. J., Abiyev, R., Ma’aitah, M. K. S., & Alt?parmak, H. (2018). Integrated artificial intelligence algorithm for skin detection. Paper presented at the ITM Web of conferences.
Chakraborty, B. K., & Bhuyan, M. (2015). Skin segmentation using possibilistic fuzzy c-means clustering in presence of skin-colored background. Paper presented at the Intelligent Computational Systems (RAICS), 2015 IEEE Recent Advances in.
Chakraborty, B. K., Bhuyan, M., & Kumar, S. (2016a). Adaptive propagation-based skin segmentation method for color images. Paper presented at the Communication (NCC), 2016 Twenty Second National Conference on.
Chakraborty, B. K., Bhuyan, M., & Kumar, S. (2016b). A Weighted Skin Probability Map for skin color segmentation. Paper presented at the Wireless Communications, Signal Processing and Networking (WiSPNET), International Conference on.
Chakraborty, B. K., Bhuyan, M., & Kumar, S. (2017). Combining image and global pixel distribution model for skin colour segmentation. Pattern Recognition Letters, 88, 33-40.
Chang, L., Jun-min, L., & Chong-xiu, Y. (2013). Skin detection using a modified self- organizing mixture network. Paper presented at the Automatic Face and Gesture Recognition (FG), 2013 10th IEEE International Conference and Workshops on.
Chaves-González, J. M., Vega-Rodríguez, M. A., Gómez-Pulido, J. A., & Sánchez- Pérez, J. M. (2010). Detecting skin in face recognition systems: A colour spaces study. Digital Signal Processing, 20(3), 806-823.
Chen, F., Hu, Z., Li, K., & Liu, W. (2016). A hybrid skin detection model from multiple color spaces based on a dual-threshold Bayesian algorithm. International Journal of Pattern Recognition and Artificial Intelligence, 30(07), 1655018.
Chen, G., Li, J., Zeng, J., Wang, B., & Lu, G. (2016). Optimizing human model reconstruction from RGB-D images based on skin detection. Virtual Reality, 20(3), 159-172.
Chen, W., Wang, K., Jiang, H., & Li, M. (2016). Skin color modeling for face detection and segmentation: a review and a new approach. Multimedia Tools and Applications, 75(2), 839-862.
Chenaoua, K., & Bouridane, A. (2010). Information theoretical based feature selection approach for human skin detection. Paper presented at the Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on.
Chenaoua, K., Kurugollu, F., & Bouridane, A. (2014). Data cleaning and outlier removal: application in human skin detection. Paper presented at the Visual Information Processing (EUVIP), 2014 5th European Workshop on.
Cheng, P., Zhang, M., & Zhou, J. (2011). Automatic Image Grading Based on Skin Segmentation. Paper presented at the Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2011 International Conference on.
Dong, B., & Wang, X. (2016). Comparison deep learning method to traditional methods using for network intrusion detection. Paper presented at the 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN).
Dong, M., Yin, L., Guo, J., Deng, W., & Xu, W. (2012). Integrative labeling based statistical color models with application to skin detection. Paper presented at the Image Processing (ICIP), 2012 19th IEEE International Conference on.
DUMITRESCU, C. M., & Dumitrache, I. (2016). HUMAN SKIN DETECTION USING NEURAL NETWORKS AND BLOCK PROCESSING TECHNIQUES. UNIVERSITY POLITEHNICA OF BUCHAREST SCIENTIFIC BULLETIN SERIES C-ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 78(1), 87-102.
El Fkihi, S., Daoudi, M., & Aboutajdine, D. (2008). The mixture of K-Optimal- Spanning-Trees based probability approximation: Application to skin detection. Image and Vision Computing, 26(12), 1574-1590.
Elmasry, W., Akbulut, A., & Zaim, A. H. (2018). Deep Learning Approaches for Predictive Masquerade Detection. Security and Communication Networks, 2018.
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115-118.
Farooq, M. A., Azhar, M. A. M., & Raza, R. H. (2016). Automatic lesion detection system (ALDS) for skin cancer classification using SVM and neural classifiers. Paper presented at the 2016 IEEE 16th International Conference on Bioinformatics and Bioengineering (BIBE).
Farooq, M. S., Khan, M. A., Abbas, S., Athar, A., Ali, N., & Hassan, A. (2019). Skin Detection based Pornography Filtering using Adaptive Back Propagation Neural Network. Paper presented at the 2019 8th International Conference on Information and Communication Technologies (ICICT).
Fatahi, M., Nadjafi, M., & Makki, S. V. A.-D. (2013). Improving the performance of skin segmentation in quasi-skin regions via multiple classifier system. Paper presented at the Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on.
Ferrer, M., Morales, A., Travieso, C., & Alonso, J. (2012). Wide band spectroscopic skin detection for contactless hand biometrics. IET computer vision, 6(5), 415- 424.
Filali, I., Ziou, D., & Benblidia, N. (2012). Multinomial bayesian kernel logistic discriminant based method for skin detection. Paper presented at the Signal Image Technology and Internet Based Systems (SITIS), 2012 Eighth International Conference on.
Gao, Y., Tannenbaum, A., Chen, H., Torres, M., Yoshida, E., Yang, X., . . . Liu, T. (2013). Automated skin segmentation in ultrasonic evaluation of skin toxicity in breast cancer radiotherapy. Ultrasound in medicine & biology, 39(11), 2166- 2175.
Geng, J., Miao, Z., Liang, Q., & Wang, S. (2015). Rotation invariant ellipsoid projection for domain transfer in human skin detection. Paper presented at the Multimedia Signal Processing (MMSP), 2015 IEEE 17th International Workshop on.
Geng, J., Miao, Z., & Zhong, C. (2011). Skin detection with illumination adaptation in single image. Paper presented at the Multimedia and Expo (ICME), 2011 IEEE International Conference on.
Ghazali, K. H. B., Ma, J., & Xiao, R. (2012). An innovative face detection based on YCgCr color space. Physics Procedia, 25, 2116-2124.
Ghaziasgar, M., Connan, J., & Bagula, A. B. (2016). Enhanced adaptive skin detection with contextual tracking feedback. Paper presented at the Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2016.
Ghomsheh, A. N., Talebpour, A., & Basseri, M. (2011). Regional skin detection based on eliminating skin-like Lambertian surfaces. Paper presented at the Computers & Informatics (ISCI), 2011 IEEE Symposium on.
Ghouzali, S., El Aroussi, M., El Hassouni, M., Rziza, M., & Aboutajdine, D. (2010). Statistical block-based skin detection. Paper presented at the I/V Communications and Mobile Network (ISVC), 2010 5th International Symposium on.
Ghouzali, S., Hemami, S., Rziza, M., Aboutajdine, D., & Mouaddib, E. M. (2008). A skin detection algorithm based on discrete cosine transform and generalized gaussian density. Paper presented at the Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on.
Golhani, K., Balasundram, S. K., Vadamalai, G., & Pradhan, B. (2018). A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture, 5(3), 354-371.
Gonlin, V. (2020). Colorful Reflections: Skin Tone, Reflected Race, and Perceived Discrimination among Blacks, Latinxs, and Whites. RACE AND SOCIAL PROBLEMS.
Hamuda, E., Mc Ginley, B., Glavin, M., & Jones, E. (2017). Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and Electronics in Agriculture, 133, 97-107.
Han, H., Shan, S., Chen, X., & Gao, W. (2013). A comparative study on illumination preprocessing in face recognition. Pattern Recognition, 46(6), 1691-1699.
Hao-Kui, T., & Zhi-Quan, F. (2012). Skin Recognition Based on Color Image Enhance Algorithm. Paper presented at the Computer Science & Service System (CSSS), 2012 International Conference on.
Hatcher, W. G., & Yu, W. (2018). A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends. IEEE Access, 6, 24411-24432.
He, Y., Shi, J., Wang, C., Huang, H., Liu, J., Li, G., . . . Wang, J. (2019). Semi- supervised Skin Detection by Network with Mutual Guidance. Paper presented at the Proceedings of the IEEE International Conference on Computer Vision.
Hettiarachchi, R., & Peters, J. F. (2016). Multi-manifold-based skin classifier on feature space Voronoï regions for skin segmentation. Journal of Visual Communication and Image Representation, 41, 123-139.
Hu, Z., Wang, G., Lin, X., & Yan, H. (2009). Skin segmentation based on graph cuts. Tsinghua Science and Technology, 14(4), 478-486.
Huang, L., Ji, W., Wei, Z., Chen, B.-W., Yan, C. C., Nie, J., . . . Jiang, B. (2015). Robust skin detection in real-world images. Journal of Visual Communication and Image Representation, 29, 147-152.
Huang, L., Xia, T., Zhang, Y., & Lin, S. (2011). Human skin detection in images by MSER analysis. Paper presented at the Image Processing (ICIP), 2011 18th IEEE International Conference on.
Huang, T.-h., Yu, Y.-m., & Qin, X.-g. (2011). A High-Performance Skin Segmentation Method. Procedia Engineering, 15, 608-612.
Hussain, S. A.-K., & Al-Bayati, M. A. (2019). Human Face Detection with skin color properties. Technology, 10(5), 157-161.
Hwang, I., Kim, Y., & Cho, N. I. (2017a). Skin detection based on multi-seed propagation in a multi-layer graph for regional and color consistency. Paper presented at the Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on.
Hwang, I., Kim, Y., & Cho, N. I. (2017b). Skin detection based on multi-seed propagation in a multi-layer graph for regional and color consistency. Paper presented at the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Islam, A. R., Alammari, A., & Buckles, B. (2019). Skin detection in image and video founded in clustering and region growing. Paper presented at the Mobile Multimedia/Image Processing, Security, and Applications 2019.
Islam, M., Watters, P. A., & Yearwood, J. (2011). Real-time detection of children’s skin on social networking sites using Markov random field modelling. Information Security Technical Report, 16(2), 51-58.
Jati, H., & Dominic, D. D. (2008). Human skin detection using defined skin region. Paper presented at the Information Technology, 2008. ITSim 2008. International Symposium on.
Jensch, D., Mohr, D., & Zachmann, G. (2015). A Comparative Evaluation of Three Skin Color Detection Approaches. Journal of Virtual Reality and Broadcasting, 12(1).
Jiang, Z., Yao, M., & Jiang, W. (2007). Skin detection using color, texture and space information. Paper presented at the Fuzzy Systems and Knowledge Discovery, 2007. FSKD 2007. Fourth International Conference on.
Jo, Y. C., Kim, H. N., Kang, J. H., Hong, H. K., Choi, Y. S., Jung, S. W., & Kim, S. P. (2017). Novel wearable-type biometric devices based on skin tissue optics with multispectral LED–photodiode matrix. Japanese Journal of Applied Physics, 56(4S), 04CM01.
Jusoh, R. M., Hamzah, N., Marhaban, M. H., & Alias, N. M. A. (2010). Skin detection based on thresholding in RGB and hue component. Paper presented at the Industrial Electronics & Applications (ISIEA), 2010 IEEE Symposium on.
Kaabneh, K., Abu-Hammad, E., & Hamd, F. (2007). Enhanced Skin Detection Technique Using Block Matching. Paper presented at the Signal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference on.
Kawulok, M., Kawulok, J., & Nalepa, J. (2014). Spatial-based skin detection using discriminative skin-presence features. Pattern Recognition Letters, 41, 3-13.
Kawulok, M., Kawulok, J., Nalepa, J., & Papiez, M. (2013). Skin detection using spatial analysis with adaptive seed. Paper presented at the Image Processing (ICIP), 2013 20th IEEE International Conference on.
Kelly, W., Donnellan, A., & Molloy, D. (2008). Screening for objectionable images: a review of skin detection techniques. Paper presented at the Machine Vision and Image Processing Conference, 2008. IMVIP'08. International.
Ketenci, S., & Gencturk, B. (2013). Performance analysis in common color spaces of 2D Gaussian Color Model for skin segmentation. Paper presented at the EUROCON, 2013 IEEE.
Khalid, S., Jamil, U., Saleem, K., Akram, M. U., Manzoor, W., Ahmed, W., & Sohail, A. (2016). Segmentation of skin lesion using Cohen–Daubechies–Feauveau biorthogonal wavelet. SpringerPlus, 5(1), 1603.
Khan, R., Hanbury, A., Sablatnig, R., Stöttinger, J., Khan, F. A., & Khan, F. A. (2014). Systematic skin segmentation: merging spatial and non-spatial data. Multimedia tools and applications, 69(3), 717-741.
Khan, R., Hanbury, A., & Stoettinger, J. (2010). Skin detection: A random forest approach. Paper presented at the Image Processing (ICIP), 2010 17th IEEE International Conference on.
Khan, R., Hanbury, A., Stöttinger, J., Khan, F. A., Khattak, A. U., & Ali, A. (2014). Multiple color space channel fusion for skin detection. Multimedia tools and applications, 72(2), 1709-1730.
Kidono, K., Kanzawa, Y., Tagawa, T., Kojima, Y., & Naito, T. (2013). Skin segmentation using a multiband camera for early pedestrian detection. Paper presented at the Intelligent Vehicles Symposium (IV), 2013 IEEE.
Kim, Y., Hwang, I., & Cho, N. I. (2017). Convolutional neural networks and training strategies for skin detection. Paper presented at the 2017 IEEE International Conference on Image Processing (ICIP).
Klevan, K., Hanafi, M., & Ramli, A. (2014). Optimal Combination of Color Components for Skin Detection. Paper presented at the Artificial Intelligence with Applications in Engineering and Technology (ICAIET), 2014 4th International Conference on.
Kolkur, S., Kalbande, D., Shimpi, P., Bapat, C., & Jatakia, J. (2017). Human skin detection using RGB, HSV and YCbCr color models. arXiv preprint arXiv:1708.02694.
Kuiaski, D., Neto, H. V., Borba, G., & Gamba, H. (2009). A study of the effect of illumination conditions and color spaces on skin segmentation. Paper presented at the Computer Graphics and Image Processing (SIBGRAPI), 2009 XXII Brazilian Symposium on.
Kumar, A. (2014). An empirical study of selection of the appropriate color space for skin detection: A case of face detection in color images. Paper presented at the Issues and Challenges in Intelligent Computing Techniques (ICICT), 2014 International Conference on.
Labati, R. D., Genovese, A., Piuri, V., & Scotti, F. (2019). A Scheme for Fingerphoto Recognition in Smartphones Selfie Biometrics (pp. 49-66): Springer.
Lee, Y., Jang, C., & Kim, H. (2016). Accelerating a computer vision algorithm on a mobile SoC using CPU-GPU co-processing: a case study on face detection. Paper presented at the Proceedings of the International Conference on Mobile Software Engineering and Systems.
Lei, Y., Xiaoyu, W., Hui, L., Dewei, Z., & Jun, Z. (2011). An algorithm of skin detection based on texture. Paper presented at the Image and Signal Processing (CISP), 2011 4th International Congress on.
Lei, Y., Yuan, W., Wang, H., Wenhu, Y., & Bo, W. (2016). A skin segmentation algorithm based on stacked autoencoders. IEEE Transactions on Multimedia, 19(4), 740-749.
Lei, Y., Yuan, W., Wang, H., Wenhu, Y., & Bo, W. (2017). A skin segmentation algorithm based on stacked autoencoders. IEEE Transactions on Multimedia, 19(4), 740-749.
Li, B., Xue, X., & Fan, J. (2007). A robust incremental learning framework for accurate skin region segmentation in color images. Pattern Recognition, 40(12), 3621- 3632.
Lionnie, R., & Alaydrus, M. (2017). A comparison of human skin color detection for biometrie identification. Paper presented at the 2017 International Conference on Broadband Communication, Wireless Sensors and Powering (BCWSP).
Liu, Q., & Peng, G.-z. (2010). A robust skin color based face detection algorithm. Paper presented at the 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR 2010).
Luo, Y., & Guan, Y.-P. (2017). Adaptive skin detection using face location and facial structure estimation. IET Computer Vision, 11(7), 550-559.
Mahmoodi, M. R., & Sayedi, S. M. (2014). Boosting performance of face detection by using an efficient skin segmentation algorithm. Paper presented at the Information Technology and Electrical Engineering (ICITEE), 2014 6th International Conference on.
Mahmoodi, M. R., & Sayedi, S. M. (2016). A Comprehensive Survey on Human Skin Detection. International Journal of Image, Graphics & Signal Processing, 8(5).
Mahmoodi, M. R., Sayedi, S. M., & Karimi, F. (2017). Color-based skin segmentation in videos using a multi-step spatial method. Multimedia Tools and Applications, 76(7), 9785-9801.
Mahmoodi, M. R., Sayedi, S. M., Karimi, F., Fahimi, Z., Rezai, V., & Mannani, Z. (2015). SDD: A skin detection dataset for training and assessment of human skin classifiers. Paper presented at the Knowledge-Based Engineering and Innovation (KBEI), 2015 2nd International Conference on.
Medeiros, R., Scharcanski, J., & Wong, A. (2016). Image segmentation via multi-scale stochastic regional texture appearance models. Computer Vision and Image Understanding, 142, 23-36.
Mendenhall, M. J., Nunez, A. S., & Martin, R. K. (2015). Human skin detection in the visible and near infrared. Applied optics, 54(35), 10559-10570.
Mitra, S. (2013). A probabilistic method of skin detection. Paper presented at the Image Information Processing (ICIIP), 2013 IEEE Second International Conference on.
Moallem, P., Mousavi, B. S., & Monadjemi, S. A. (2011). A novel fuzzy rule base system for pose independent faces detection. Applied Soft Computing, 11(2), 1801-1810.
MOHAMMED, A. A. Q., Lv, J., & Islam, M. (2019). A Deep Learning-Based End-to- End Composite System for Hand Detection and Gesture Recognition. Sensors, 19(23), 5282.
Moradi, B., & Ezoji, M. (2015). Skin detection based on contextual information. Paper presented at the Pattern Recognition and Image Analysis (IPRIA), 2015 2nd International Conference on.
Muhammad, B., & Abu-Bakar, S. A. R. (2015). A hybrid skin color detection using HSV and YCgCr color space for face detection. Paper presented at the Signal and Image Processing Applications (ICSIPA), 2015 IEEE International Conference on.
Musa, Z., Jumari, K., & Zainal, N. (2011). A method of human skin detection base on background subtraction and color enhancement. Paper presented at the Business, Engineering and Industrial Applications (ISBEIA), 2011 IEEE Symposium on.
Mustafa, A. A., Elbashir, A. A., & Babikir, S. F. (2015). A study of color constancy methods for skin detection. Paper presented at the Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), 2015 International Conference on.
Nadian, A., & Talebpour, A. (2011). Pixel-based skin detection using sinc function. Paper presented at the Computers & Informatics (ISCI), 2011 IEEE Symposium on.
Nadimi, N., Azimifar, Z., & Ahmadi, E. (2013). Skin detection using a statistical color spaces fusion model. Paper presented at the Machine Vision and Image Processing (MVIP), 2013 8th Iranian Conference on.
Naji, S., Jalab, H. A., & Kareem, S. A. (2019). A survey on skin detection in colored images. Artificial Intelligence Review, 52(2), 1041-1087.
Naji, S. A., Zainuddin, R., & Jalab, H. A. (2012). Skin segmentation based on multi pixel color clustering models. Digital Signal Processing, 22(6), 933-940.
Nallaperumal, K., Ravi, S., Babu, C. N. K., Selvakumar, R., Fred, A. L., Christopher, S., & Vinsley, S. (2007). Skin detection using color pixel classification with application to face detection: A comparative study. Paper presented at the iccima.
Ng, P., & Pun, C.-M. (2012). Skin segmentation based on human face illumination feature. Paper presented at the Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology-Volume 03.
Nguyen-Trang, T. (2018). A new efficient approach to detect skin in color image using Bayesian classifier and connected component algorithm. Mathematical Problems in Engineering, 2018.
Oghaz, M. M., Maarof, M. A., Zainal, A., Rohani, M. F., & Yaghoubyan, S. H. (2015). A hybrid color space for skin detection using genetic algorithm heuristic search and principal component analysis technique. PLoS ONE, 10(8), e0134828.
Omanovic, S., Buza, E., & Besic, I. (2014). RGB ratios based skin detection. Paper presented at the Information and Communication Technology, Electronics and Microelectronics (MIPRO), 2014 37th International Convention on.
Osman, M. Z., Maarof, M. A., & Rohani, M. F. (2014). Improved skin detection based on dynamic threshold using multi-colour space. Paper presented at the Biometrics and Security Technologies (ISBAST), 2014 International Symposium on.
Patil, S., & Udupi, V. (2015). Using Histogram Specification in a Hybrid Preprocessing Technique for Segmentation of Malignant Skin Lesions from Dermoscopic Images. International Journal of Computer Science Engineering and Information Technology Research, 5(4), 71-82.
Perez, M., Avila, S., Moreira, D., Moraes, D., Testoni, V., Valle, E., . . . Rocha, A. (2017). Video pornography detection through deep learning techniques and motion information. Neurocomputing, 230, 279-293.
Powar, V., Kulkami, A., Lokare, R., & Lonkar, A. (2013). Skin detection for forensic investigation. Paper presented at the Computer Communication and Informatics (ICCCI), 2013 International Conference on.
Raghunandan, A., Raghav, P., & Aradhya, H. R. (2018). Object detection algorithms for video surveillance applications. Paper presented at the 2018 International Conference on Communication and Signal Processing (ICCSP).
Rahman, M. A., Purnama, I. K. E., & Purnomo, M. H. (2014). Simple method of human skin detection using HSV and YCbCr color spaces. Paper presented at the Intelligent Autonomous Agents, Networks and Systems (INAGENTSYS), 2014 IEEE International Conference on.
Razmjooy, N., Mousavi, B. S., & Soleymani, F. (2013). A hybrid neural network Imperialist Competitive Algorithm for skin color segmentation. Mathematical and Computer Modelling, 57(3-4), 848-856.
Roy, K., Mohanty, A., & Sahay, R. R. (2017). Deep learning based hand detection in cluttered environment using skin segmentation. Paper presented at the Proceedings of the IEEE International Conference on Computer Vision Workshops.
Saar, B. G., Freudiger, C. W., Reichman, J., Stanley, C. M., Holtom, G. R., & Xie, X. S. (2010). Video-rate molecular imaging in vivo with stimulated Raman scattering. science, 330(6009), 1368-1370.
Sahu, P., Yu, D., & Qin, H. (2018). Apply lightweight deep learning on internet of things for low-cost and easy-to-access skin cancer detection. Paper presented at the Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications.
Salimi, F., Farzin, H., & Ebrahimi, F. (2012). A novel skin detection method using Generalized Discriminant Analysis. Paper presented at the Computer Systems and Industrial Informatics (ICCSII), 2012 International Conference on.
Sandnes, F. E., Neyse, L., & Huang, Y.-P. (2016). Simple and practical skin detection with static RGB-color lookup tables: A visualization-based study. Paper presented at the Systems, Man, and Cybernetics (SMC), 2016 IEEE International Conference on.
Sarkar, A., Abbott, A. L., & Doerzaph, Z. (2017). Universal Skin Detection Without Color Information. Paper presented at the Applications of Computer Vision (WACV), 2017 IEEE Winter Conference on.
Sawicki, D. J., & Miziolek, W. (2015). Human colour skin detection in CMYK colour space. IET Image Processing, 9(9), 751-757.
Saxen, F., & Al-Hamadi, A. (2014a). Color-based skin segmentation: An evaluation of the state of the art. Paper presented at the ICIP.
Saxen, F., & Al-Hamadi, A. (2014b). Color-based skin segmentation: An evaluation of the state of the art. Paper presented at the 2014 IEEE International Conference on Image Processing (ICIP).
Selamat, A., Maarof, M. A., & Chin, T. Y. (2009). Fuzzy Mamdani Inference System Skin Detection. Paper presented at the Hybrid Intelligent Systems, 2009. HIS'09. Ninth International Conference on.
Shaik, K. B., Ganesan, P., Kalist, V., Sathish, B., & Jenitha, J. M. M. (2015a). Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Computer Science, 57, 41-48.
Shaik, K. B., Ganesan, P., Kalist, V., Sathish, B., & Jenitha, J. M. M. (2015b). Comparative study of skin color detection and segmentation in HSV and YCbCr color space. Procedia Computer Science, 57(12), 41-48.
Shemshaki, M., & Amjadifard, R. (2011). Skin detection using fuzzy rule base system. Paper presented at the Automation, Robotics and Applications (ICARA), 2011 5th International Conference on.
Shirali-Shahreza, S., & Mousavi, M. (2008). A new Bayesian classifier for skin detection. Paper presented at the Innovative Computing Information and Control, 2008. ICICIC'08. 3rd International Conference on.
Shoyaib, M., Abdullah-Al-Wadud, M., & Chae, O. (2012). A skin detection approach based on the Dempster–Shafer theory of evidence. International Journal of Approximate Reasoning, 53(4), 636-659.
Shruthi, M., & Harsha, B. (2013). Non-parametric histogram based skin modeling for skin detection. Paper presented at the Computational Intelligence and Computing Research (ICCIC), 2013 IEEE International Conference on.
Soleimanizadeh, S., Mohamad, D., Saba, T., & Rehman, A. (2015). Recognition of partially occluded objects based on the three different color spaces (RGB, YCbCr, HSV). 3D Research, 6(3), 22.
Song, W., Wu, D., Xi, Y., Park, Y. W., & Cho, K. (2017). Motion-based skin region of interest detection with a real-time connected component labeling algorithm. Multimedia tools and applications, 76(9), 11199-11214.
Soria-Frisch, A., Verschae, R., & Olano, A. (2007). Fuzzy fusion for skin detection. Fuzzy Sets and Systems, 158(3), 325-336.
Sumithra, R., Suhil, M., & Guru, D. (2015). Segmentation and classification of skin lesions for disease diagnosis. Procedia Computer Science, 45, 76-85.
Sun, H.-M. (2010). Skin detection for single images using dynamic skin color modeling. Pattern recognition, 43(4), 1413-1420.
Swaney, C., Akusok, A., Björk, K.-M., Miche, Y., & Lendasse, A. (2015). Efficient skin segmentation via neural networks: HP-ELM and BD-SOM. Procedia Computer Science, 53, 400-409.
Tavallali, P., & Yazdi, M. (2015). Robust skin detector based on AdaBoost and statistical luminance features. Paper presented at the Technology, Communication and Knowledge (ICTCK), 2015 International Congress on.
Teodoro, B. T., Bernardes, J., & Digiampietri, L. A. (2017). Skin Color Segmentation and Levenshtein Distance Recognition of BSL Signs in Video. Paper presented at the 2017 30th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI).
Tomaschitz, J. A., & Facon, J. (2009). Skin detection applied to multi-racial images. Paper presented at the Systems, Signals and Image Processing, 2009. IWSSIP 2009. 16th International Conference on.
Ungureanu, A.-S., Javidnia, H., Costache, C., & Corcoran, P. (2016a). A review and comparative study of skin segmentation techniques for handheld imaging
devices. Paper presented at the 2016 IEEE International Conference on Consumer Electronics (ICCE).
Ungureanu, A.-S., Javidnia, H., Costache, C., & Corcoran, P. (2016b). A review and comparative study of skin segmentation techniques for handheld imaging devices. Paper presented at the Consumer Electronics (ICCE), 2016 IEEE International Conference on.
Vinayakumar, R., Alazab, M., Soman, K., Poornachandran, P., Al-Nemrat, A., & Venkatraman, S. (2019). Deep learning approach for intelligent intrusion detection system. IEEE Access, 7, 41525-41550.
White, K. M. (2015). The salience of skin tone: Effects on the exercise of police enforcement authority. Ethnic and Racial Studies, 38(6), 993-1010.
Wu, Q., Cai, R., Fan, L., Ruan, C., & Leng, G. (2012). Skin detection using color processing mechanism inspired by the visual system.
Yadav, S., & Nain, N. (2016). A novel approach for face detection using hybrid skin color model. Journal of Reliable Intelligent Environments, 2(3), 145-158.
Yas, Q. M., Zadain, A., Zaidan, B., Lakulu, M., & Rahmatullah, B. (2017). Towards on develop a framework for the evaluation and benchmarking of skin detectors based on artificial intelligent models using multi-criteria decision-making techniques. International Journal of Pattern Recognition and Artificial Intelligence, 31(03), 1759002.
Yas, Q. M., Zaidan, A., Zaidan, B., Rahmatullah, B., & Karim, H. A. (2018). Comprehensive insights into evaluation and benchmarking of real-time skin detectors: Review, open issues & challenges, and recommended solutions. Measurement, 114, 243-260.
Yogarajah, P., Condell, J., Curran, K., McKevitt, P., & Cheddad, A. (2011). A dynamic threshold approach for skin tone detection in colour images. International Journal of Biometrics, 4(1), 38-55.
Yong-jia, Z., Shu-ling, D., & Xiao, X. (2008). A Mumford-Shah level-set approach for skin segmentation using a new color space. Paper presented at the System Simulation and Scientific Computing, 2008. ICSC 2008. Asia Simulation Conference-7th International Conference on.
Yu, J.-J., & Han, S.-W. (2014). Skin detection for adult image identification. Paper presented at the Advanced Communication Technology (ICACT), 2014 16th International Conference on.
Yu, Z. (2006). Fast Gaussian mixture clustering for skin detection. Paper presented at the Image Processing, 2006 IEEE International Conference on.
Yun, L., & Chuan-xu, W. (2008). An improved algorithm of human skin detection in video image based on linear combination of 2-order Markov and Wiener predictor. Paper presented at the Computer Science and Computational Technology, 2008. ISCSCT'08. International Symposium on.
Zafarifar, B., & Bellers, E. B. (2013). Application and evaluation of texture-adaptive skin detection in TV image enhancement. Paper presented at the Consumer Electronics (ICCE), 2013 IEEE International Conference on.
Zafarifar, B., Martinière, A., & de With, P. H. (2010). Improved skin segmentation for TV image enhancement, using color and texture features. Paper presented at the Consumer Electronics (ICCE), 2010 Digest of Technical Papers International Conference on.
Zafarifar, B., & Van Den Kerkhof, T. (2012). Texture-adaptive skin detection for TV and its real-time implementation on DSP and FPGA. IEEE Transactions on Consumer Electronics, 58(1).
Zaidan, A., Ahmad, N. N., Karim, H. A., Larbani, M., Zaidan, B., & Sali, A. (2014a). Image skin segmentation based on multi-agent learning Bayesian and neural network. Engineering Applications of Artificial Intelligence, 32, 136-150.
Zaidan, A., Ahmad, N. N., Karim, H. A., Larbani, M., Zaidan, B., & Sali, A. (2014b). On the multi-agent learning neural and Bayesian methods in skin detector and pornography classifier: An automated anti-pornography system. Neurocomputing, 131, 397-418.
Zaidan, A., Zaidan, B., Alsalem, M., Albahri, O., Albahri, A., & Qahtan, M. (2019). Multi-agent learning neural network and Bayesian model for real-time IoT skin detectors: a new evaluation and benchmarking methodology. Neural Computing and Applications, 1-52.
Zhang, J., Zhuang, Z., & Geng, W. (2014). Local pixel wise skin detection algorithm based on FPGA. Paper presented at the Information Technology and Artificial Intelligence Conference (ITAIC), 2014 IEEE 7th Joint International.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213-237.
Zhao, S., Zhuo, L., Xiao, Z., & Shen, L. (2009). A data-mining based skin detection method in JPEG compressed domain. Paper presented at the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery.
Zhengming, L., Tong, Z., & Jin, Z. (2010). Skin detection in color images. Paper presented at the Computer Engineering and Technology (ICCET), 2010 2nd International Conference on.
Zhongdong, W., Saichao, W., & Zichao, H. (2013). A Bayesian approach to skin detection in YCbCr color space. Paper presented at the Awareness Science and Technology and Ubi-Media Computing (iCAST-UMEDIA), 2013 International Joint Conference on.
Zhou, Y., Jiang, G., & Lin, Y. (2016). A novel finger and hand pose estimation technique for real-time hand gesture recognition. Pattern Recognition, 49, 102- 114.
Zhu, J.-q., & Cai, C.-h. (2011). Region growing based high brightness skin detection. Paper presented at the Signals, Circuits and Systems (ISSCS), 2011 10th International Symposium on.
Zuo, H., Fan, H., Blasch, E., & Ling, H. (2017). Combining convolutional and recurrent neural networks for human skin detection. IEEE Signal Processing Letters, 24(3), 289-293.
|
| This material may be protected under Copyright Act which governs the making of photocopies or reproductions of copyrighted materials. You may use the digitized material for private study, scholarship, or research. |