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We propose a novel method for real-time face verification on mobile platforms such as PDAs and cell-phones. To implement the real-time system, a fixed-point arithmetic is used, and the face components are extracted by using a fast boosting algorithm. In addition, an image reduction method is adapted by using a pre-calculated look-up table, and integral image calculation method is modified to reduce the processing time. Also, the efficient valid coefficients of a DCT transformed image in the face region are calculated, and the face features are extracted by using the EP-LDA methods. In experiments, the usefulness of the proposed method has been demonstrated on a smart phone with high face verification performances. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel face recognition method using the effective transform coefficients of face images. The method is based on extraction of effective vector in Discrete Cosine Transform (DCT) domain and Linear Discriminant Analysis (LDA) of effective vector. In general, face images have characteristics that they show larger energy congestion in horizontal frequency coefficients than in vertical or diagonal frequency coefficients. However, many previous methods have shortcomings that they don’t utilize the facial energy characteristics. To overcome shortcomings above, the proposed method selects the effective coefficients of the face in DCT domain and then extracts feature vector through LDA analysis on DCT coefficients. Experimental results show that our method has improvements of recognition performance over the previous methods. [ABSTRACT FROM AUTHOR]

In this paper, we propose an algorithm for detecting a face in a target image using sketch operators and vertical facial symmetry (VFS). The former are operators which effectively reflect perceptual characteristics of human visual system to compute sketchiness of pixels and the latter means the bilateral symmetry which a face shows about its central longitudinal axis. In the proposed algorithm, horizontal and vertical sketch images are first obtained from a target image by using a directional BDIP (block difference inverse probabilities) operator which is modified from the BDIP operator. The pair of sketch images is next transformed into a generalized symmetry magnitude (GSM) image by the generalized symmetry transform (GST). From the GSM image, face candidates are then extracted which are quadrangular regions enclosing the triangles that satisfy eyes-mouth triangle (EMT) conditions and VFS. The sketch image for each candidate is obtained by the BDIP operator and classified into a face or nonface by the Bayesian classifier. Among the face candidates classified into faces, one with the largest VFS becomes the output where the EMT gives the location of two eyes and a mouth of a target face. If the procedure detects no face, then it is executed again after illumination compensation on the target image. Experimental results for 1,000 320x240 target images of various backgrounds and circumstances show that the proposed method yields about 97% detection rate and takes a time less than 0.25 second per target image. [ABSTRACT FROM AUTHOR]