Total views : 288
ILUT based Skin Colour Modelling for Human Detection
Objectives: Numerous techniques have been proposed in past for skin colour modelling and some out of these are utilized in human detection problem. This paper proposes a novel technique which uses Indexed Look-up-table (ILUT) for skin colour modelling. Methods: In the proposed technique, skin colour cluster on 2-dimensional Cb-Cr plane in YCbCr colour space is modelled with an ILUT. ILUT contains the lower and upper bounds of Cb values corresponding to each value of Cr in the skin colour cluster. Outliers in the cluster are removed by applying median filter, because they contribute to the wrong classification of skin colour and hence wrong detection. Findings: The proposed technique delivers reasonably good performance on True Positive Rate (TPR), False Positive Rate (FPR) and Accuracy parameters. Classification accuracy of 89.89% for proposed technique is almost comparable to that of other techniques in literature. Comparative results are shown in Table 2 of the paper. Classification complexity is one parameter on which the proposed technique outperforms the rest of the other skin colour modelling techniques and it is of O(MxN). MxN is the image resolution. Skin colour modelling using ILUT is also efficient in terms of space requirements as compared to other non-parametric methods of skin colour modelling. Applications: Least classification complexity makes this technique most appropriate for real time systems/ applications for detecting presence of human being.
Classification Accuracy, Classification Complexity, Human Detection, Indexed Look-up-table, Skin Colour Modelling.
- Praveen K, Makrogiannis S, Bourbakis N. A survey of skin-color modeling and detection methods. Pattern Recognition. 2007; 40(3):1106–22.
- Benjamin DZ, Super BJ, Quek FHQ. Comparison of five color models in skin pixel classification. Proceedings International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, Washington; 1999. p. 1–6.
- Tan WR, Chan CS, Yogarajah P, Condell J. A fusion approach for efficient human skin detection. IEEE Transactions on Industrial Informatics. 2012; 8(1):138–47.
- Vadakkepat P, Lim P, De Silva LC, Jing L, Ling LL. Multimodal approach to human-face detection and tracking. IEEE Transactions on Industrial Electronics. 2008; 5(3):1385–93.
- Ganesan P, Rajini V, Sathish BS, Kalist V, Basha SKK. Satellite image segmentation based on YCbCr color space. Indian Journal of Science and Technology. 2015; 8(1):35–41. DOI: 10.17485/ijst/2015/v8i1/51281.
- Mehdi N, Sepidname G, Eizi A, Amani A. A new skin color detection approach based on fuzzy expert system. Indian Journal of Science and Technology. 2015; 8(21):1–11. DOI: 10.17485/ijst/2015/v8i21/50606.
- Wang CW, Ke, Ming L. Skin color modeling for face detection and segmentation: a review and a new approach. Multimedia Tools and Applications. 2014; 74(1):321–45.
- Peter P, Solina F. An automatic human face detection method; 1999. p. 122–30.
- Borut B, Solina F, Peer P. 15 seconds of fame: an interactive, computer-vision based art installation. Proceedings of the 12th annual ACM International Conference on Multimedia. 2004; 1:198–204.
- Wei-Che C, Wang MS. Region-based and content adaptive skin detection in color images. International Journal of Pattern Recognition and Artificial Intelligence. 2007; 21(5):831–53.
- Esmaeil K, Tabatabaie ZS. A hybrid face detection approach in color images with complex background. Indian Journal of Science and Technology. 2015; 8(1):49. DOI: 10.17485/ijst/2015/v8i1/51337.
- Selin MB, Bulut M, Atalay V. Projection based method for segmentation of human face and its evaluation. Pattern Recognition Letters. 2002; 23(14):1623–9.
- Lam PS, Chai D, Bouzerdoum A. A universal and robust human skin color model using neural networks. International Joint Conference on Neural Networks. IJCNN'01, Washington. 2001; 4:2844–9.
- Alaa YT, Jalab HA. Increasing the reliability of skin detectors. Scientific Research and Essays. 2010; 5(17):2480–90.
- Jinguang H, Awad G, Sutherland A. Automatic skin segmentation and tracking in sign language recognition. Computer Vision, IET. 2009; 3(1):24–35.
- Rafael CG, Woods RE, Eddins SL. Digital image processing using MATLAB. Pearson Education India; 2004.
- Michael JJ, Rehg JM. Statistical color models with application to skin detection. International Journal of Computer Vision. 2002; 46(1):81–96.
- Douglas C, Phung SL, Bouzerdoum A. A Bayesian skin/non-skin color classifier using non-parametric density estimation. Proceedings of the 2003 International Symposium on Circuits and Systems. ISCAS'03. 2003; 2:464–7.
- Zhanyu M, Leijon A. Human skin color detection in RGB space with Bayesian estimation of beta mixture models.18th European Signal Processing Conference (EUSIPCO-2010), Aalborg; 2010. p. 1204–8.
- Vladimir V, Sazonov V, Andreeva A. A survey on pixel-based skin color detection techniques. Proceedings on Graphic. 2003; 3:1–7.
- Rein-Lien H, Abdel-Mottaleb M, Jain AK. Face detection in color images. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2002; 24(5):696–706.
- Bernd M, Wien M. Segmentation and tracking of facial regions in color image sequences. Visual Communications and Image Processing 2000; 2000.
- Frank YS, Cheng S, Chuang C-F, Wang PSP. Extracting faces and facial features from color images. International Journal of Pattern Recognition and Artificial Intelligence. 2008; 22(3):515–34.
- Jae YL, Yoo SI. An elliptical boundary model for skin color detection. Proceedings of the 2002 International Conference on Imaging Science, Systems, and Technology; 2002.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.