Knowledge-based interpretation of outdoor natural color scenes
Knowledge-based interpretation of outdoor natural color scenes
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Face Detection From Color Images Using a Fuzzy Pattern Matching Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Digital Image Processing
Symmetric Shape-from-Shading Using Self-ratio Image
International Journal of Computer Vision
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
SIGGRAPH '78 Proceedings of the 5th annual conference on Computer graphics and interactive techniques
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Face detection using quantized skin color regions merging andwavelet packet analysis
IEEE Transactions on Multimedia
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
Modeling focus of attention for meeting indexing based on multiple cues
IEEE Transactions on Neural Networks
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Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc.. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I1I2I3, HSI, and rgb) by evaluating 7 color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB, and RGB. Experimental results using 1,800 FERET R, G, B images corresponding to 200 subjects show that some color configurations, such as R in the RGB color space and V in the HSV color space, help improve face retrieval performance.