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Face recognition has received growing attention because of its wide applications. In this paper, an efficient face recognition algorithm based on non-negative matrix factorization (NMF) and SVM is proposed. The high dimension face images are first projected into a lower-dimensional subspace using NMF. The SVM classifier is then used to classify the face image into different classes. The experimental...
This paper investigates the possibility that uses Scale-Invariance Feature Transform (SIFT) feature for face identification. However, it is impossible to employ these SIFT keys,i.e. feature vectors, for identification directly, due to the space incompatible of such SIFT keys. To this end, the Bag-of-words (Bow) vector quantization introduced from scene or text classification is conducted for unifying...
Multimodal biometric identification technique utilizes two or more individual modalities to improve the identification accuracy and overcome some problems existing in conventional unimodal methods. This paper presents a multimodal biometric identification approach based on the features of face and palmprint. Two feature extraction methods are employed, one is based on the statistics properties (SP)...
Combined with Two-Direction Image Matrix based Principal Component Analysis (2DIMPCA) and Multiple Discriminant Analysis (MDA), a new approach for human recognition is presented based on integrating information from gait and side face at the feature level. Feature exaction and dimension reducing is done to Gait Energy Image (GEI) and Side Face Image (SFI) respectively by 2DIMPCA, and two original...
Multimodal biometric system utilizes two or moreindividual modalities, e.g., face, ear, and fingerprint, toimprove the recognition accuracy of conventional unimodalmethods. We propose a new dimensionality reduction methodcalled Dimension Reduce Projection (DRP) in this paper. DRPcan not only preserve local information by capturing the intramodal geometry, but also extract between-class relevant structures...
In this paper, the algorithm of face detection based on AdaBoost was studied, which is the multi-classifier cascade face detection algorithm and can attain real-time face detection in the system. It is a algorithm to automatically generate cascade classifier in the training process, which can effectively avoid the phenomenon of over trained. Compare to traditional AdaBoost face detection algorithm...
Studies on learning problems from geometry perspective have attracted an ever increasing attention in machine learning, leaded by Biomimetic Pattern Recognition on information geometry. Biomimetic Pattern Recognition is a new model of Pattern Recognition based on “matter cognition” instead of “matter classification”. This new model is much closer to the function of human being, than traditional statistical...
In this paper, a fast infrared face recognition system using Curvelet transformation is proposed. Firstly, to get the good performance of infrared face recognition from the biological feature, thermal images are converted into blood perfusion domain by blood perfusion model. Secondly, Curvelet transform has better directional and edge representation abilities than widely used wavelet transformation...
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