Book ChapterDOI
Enhancing Face Recognition Under Unconstrained Background Clutter Using Color Based Segmentation
Ankush Chatterjee,Deepak Mishra,Sai Subrahmanyam Gorthi +2 more
- pp 51-62
Reads0
Chats0
TLDR
A way to combine 3 subspace learning algorithms, namely Eigenfaces, 2 dimensional Principal Component Analysis and Row Column 2DPCA with a color-based segmentation approach in order to boost the recognition rates under unconstrained scene conditions is proposed.Abstract:
Face recognition algorithms have been extensively researched for the last 3 decades or so. Even after years of research, the algorithms developed achieve practical success only under controlled environments. Their performance usually takes a dip under unconstrained scene conditions like the presence of background clutter, non-uniform illumination etc. This paper explores the contrast in performance of standard recognition algorithms under controlled and uncontrolled environments. It proposes a way to combine 3 subspace learning algorithms, namely Eigenfaces (1DPCA), 2 dimensional Principal Component Analysis (2DPCA) and Row Column 2DPCA (RC2DPCA) with a color-based segmentation approach in order to boost the recognition rates under unconstrained scene conditions. A series of steps are performed that extract all possible facial regions from an image, following which the algorithm segregates the largest candidate for a probable face, and puts a bounding box on the blob in order to isolate only the face. It was found that the proposed algorithms, formed by the combination of such segmentation methods obtain a higher level of accuracy than the standard recognition techniques. Moreover, it serves as a general framework wherein much more robust recognition techniques could be combined to achieve boosted accuracies.read more
Citations
More filters
Improvement of Face Recognition Approach through Fuzzy-Based SVM
TL;DR: In this investigation, automatic face recognition algorithms are discussed and a combination of learning algorithms with supervision are realized to address the effects of asymmetric classes and the adaptive coefficients are employed.
References
More filters
Journal ArticleDOI
Face recognition by independent component analysis
TL;DR: Independent component analysis (ICA), a generalization of PCA, was used, using a version of ICA derived from the principle of optimal information transfer through sigmoidal neurons, which was superior to representations based on PCA for recognizing faces across days and changes in expression.
Journal ArticleDOI
Face segmentation using skin-color map in videophone applications
Douglas Chai,King Ngi Ngan +1 more
TL;DR: It is explained how the face-segmentation results can be used to improve the perceptual quality of a videophone sequence encoded by the H.261-compliant coder.
Proceedings ArticleDOI
Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods
TL;DR: Experimental results show that kernel methods provide better representations and achieve lower error rates for face recognition, which are compared with classical algorithms such as Eigenface, Fisherface, ICA, and Support Vector Machine.
Journal ArticleDOI
Face recognition using kernel principal component analysis
TL;DR: Through adopting a polynomial kernel, the principal components can be computed within the space spanned by high-order correlations of input pixels making up a facial image, thereby producing a good performance.
Journal ArticleDOI
An improved face recognition technique based on modular PCA approach
TL;DR: The proposed algorithm when compared with conventional PCA algorithm has an improved recognition rate for face images with large variations in lighting direction and facial expression and is expected to be able to cope with these variations.