A Novel Approach to Using Color Information in Improving Face Recognition Systems Based on Multi-Layer Neural Networks

Nowadays, machine-vision applications are acquiring more attention than ever due to the popularity of artificial intelligence in general which is growing bigger every day. But, although machines today are more intelligent than ever, artificial intelligence is still in its infancy. Advances in artificial intelligence promise to benefit vast numbers of applications. Some even go way beyond that to say that when artificial intelligence reaches a certain level of progress, it will be the key to the next economical revolution after the agricultural and industrial revolutions (Casti, 2008). In any case, machine-vision applications involving the human face are of major importance, since the face is the natural and most important interface used by humans. Many reasons lie behind the importance of the face as an interface. For starters, the face contains a set of features that uniquely identify each person more than any other part in the body. The face also contains main means of communications, some of which are obvious such as the eyes as image receptors and the lips as voice emitters, and some of which are less obvious such as the eye movement, the lip movement the color change in the skin, and face gestures. Basic applications involve face detection, face recognition and mood detection, and more advanced applications involve lip reading, basic temperature diagnoses, lye detection, etc. As the demand on more advanced and more robust applications increase, the conventional use of gray-scaled images in machine-vision applications in general, and specifically applications that involve the face is no longer sufficient. Color information is becoming a must. It is surprising that until recent study demonstrated that color information makes contribution and enhances robustness in face recognition. The common belief was the contrary (Yip & Sinha, 2001). Thus, gray-scaled images were used to reduce processing cost (Inooka, et al., 1999; Nefian, 2002; Ma & Khorasani, 2004; Zheng, et al., 2006; Zuo, et al., 2006; Liu & Chen, 2007). Simply speaking, we know from nature that animals relying more on their vision as a means of survival tend to see in colors. For example, some birds are able to see a wider color spectrum than humans due to their need to locate and identify objects from very high distances. The truth is that due to its nature, color can be thought of as a natural efficiency trick that gives high definition accuracy with relatively little processing cost as will be shown later in this article. Up to a certain point in the past, a simple yes or no to a still image of a face with tolerable size and rotation restrictions was good enough for O pe n A cc es s D at ab as e w w w .in te ch w eb .o rg