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Face recognition is one of the most challenging tasks in the pattern recognition field. It has many applications such as person identification; human computer interaction; security systems and video surveillance.
Face recognition from a biological point of view has been a topic widely studied by neuroscientists. Physiological researches have indicated that in the human brain we posses some concrete face detector cells for face recognition, specifically placed in the inferotemporal cortex and also spread over the frontal right hemisphere (Ferrer, 2005).
Engineers have found many of the psychophysics and neurophysiologic disclosures relevant when trying to implement an automatic face detection/recognition system. Some of those disclosures, directly extracted from the study carried out by Zhang, Samaras, and Goldstein (2005) are exposed next:
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Human recognition system uses a broad spectrum of stimuli, especially those that come from the visual, auditory and olfactory senses.
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Face perception is a mixture of both holistic and feature analysis. For adults, the brain tries first a holistic approach with a posterior refinement carried out taking on account the individual facial features. On the other hand, children pay attention mainly to isolated features.
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Spatial frequency analysis plays an important role in face detection (Ferrer, 2005).
HUMAN face recognition has become a very active research area in recent years mainly due to increasing security demands and its potential commercial and law enforcement applications. Numerous approaches have been proposed for face recognition and considerable successes have been reported (Chellappa, Wilson, & Sirohey, 1995).However, it is still a difficult task for a machine to recognize human faces accurately in real-time, especially under variable circumstances such as variations in illumination, pose, facial expression, makeup, etc. The similarity of human faces and the unpredictable variations are the greatest obstacles in face recognition (Er, Chen, & Wu 2005).
During the past thirty years, many face recognition techniques have been proposed, motivated by the increased number of real-world applications requiring the recognition of human faces (Lu et al., 2007).
Generally speaking, face recognition approaches can be divided into feature-based, template-based, the appearance-based methods like statistics-based and neural network-based categories (Chang et al., 2008). Feature-based approaches are based on the geometrical relationships of invariant salient features of the face, such as eyes, eyebrows, mouth; nose (Chang et al., 2008). The recognition rate of the feature based techniques is highly depended on the correctness of the detected invariant salient features (Chang et al., 2008). Unfortunately, the variations of illumination and facial expression will affect the detection of invariant salient features (Chang et al., 2008). Template-based approaches are based on similarity measurement of two feature sets, which can be calculated without the pairing of the invariant salient features. The drawback of the template-based method is the recognition results are highly depended on the variation of scale, pose and shape (Chang et al., 2008).
The appearance-based methods aim to learn the models from a set of training images. Neural networks, Hidden Markov model, and support vector machines are frequently adopted as the learning machines (Er et al., 2005).