Face detection is a fundamental and important research theme in the topic of Pattern Recognition and Computer Vision. Now, remarkable fruits have been achieved. Among these methods, statistics based methods hold a dominant position. In this paper, Adaboost algorithm based on Haar-like features is used to detect faces in complex background. The method combining YCbCr skin model detection and Adaboost is researched, the skin detection method is used to validate the detection results obtained by Adaboost algorithm. It overcomes false detection problem by Adaboost. Experimental results show that nearly all non-face areas are removed, and improve the detection rate.
More recently, Local Binary Patterns(LBP) has received much attention in face representation and recognition. The original LBP operator could describe the spatial structure information, which are the variety edge or variety angle features of local facial images essentially, they are important factors of classify different faces. But the scale and orientation of the edge features include more detail information which could be used to classify different persons efficiently, while original LBP operator could not to extract the information. In this paper, based on the introduction of original LBP-based facial representation and recognition, the histogram sequences of local Gabor binary patterns are used to representation facial image. Principal Component Analysis (PCA) method is used to classification the histogram sequences, which have been converted to vectors. Recognition experimental results show that the method we used in this paper increases nearly 6% than the classification performance of original LBP operator.
Infrared image offers the main advantage over visible image of being invariant to illumination changes for face
recognition. In this paper, based on the introduction of main methods of linear subspace analysis, such as Principal
Component Analysis (PCA) , Linear Discriminant Analysis(LDA) and Fast Independent Component Analysis
(FastICA),the application of these methods to the recognition of infrared face images offered by OTCBVS workshop are
investigated, and the advantages and disadvantages are compared. Experimental results show that the combination
approach of PCA and LDA leads to better classification performance than single PCA approach or LDA approach, while
the FastICA approach leads to the best classification performance with the improvement of nearly 5% compared with the
combination approach.
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