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This report presents results from the Video Person Recognition Evaluation held in conjunction with the 11th IEEE International Conference on Automatic Face and Gesture Recognition. Two experiments required algorithms to recognize people in videos from the Point-and-Shoot Face Recognition Challenge Problem (PaSC). The first consisted of videos from a tripod mounted high quality video camera. The second...
In order to solve the small sample problems and the linear inseparable problems caused by some nonlinear factors, this paper proposed a method to generate multiple virtual samples similar to the original images by its class, then all virtual samples were combined as a new database for training. The method not only helps to increase more samples, but strengthens the reliance of virtual samples on the...
A gender classification system uses human face from a given image to tell the gender of the given person. An effective gender classification approach is able to promote the improvement of many other applications, including image/video retrieval, security monitor, human-computer interaction, etc. In this paper, a method for gender classification task in frontal face images based on stacked-autoencoders...
A new algorithm for learning binary codes is presented using randomized initial assignments of bit labels to classes followed by iterative refinement to minimize intraclass Hamming distance. This Randomized Intraclass-Distance Minimizing Binary Codes (RIDMBC) algorithm is introduced in the context of face recognition, an area of biometrics where binary codes have rarely been used (unlike iris recognition)...
When kernel principal component analysis (KPCA) is applied for pattern classification problems such as face recognition, the more training samples are not leading to the easier way to get the useful principal components for classification. The reason is there are some samples containing lots of redundant information which is not conducive to classification and must be excluded from the training samples...
We present a face recognition method based on sparse representation for recognizing 3D face meshes under expressions using low-level geometric features. First, to enable the application of the sparse representation framework, we develop a uniform remeshing scheme to establish a consistent sampling pattern across 3D faces. To handle facial expressions, we design a feature pooling and ranking scheme...
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