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The term soft biometrics typically refers to attributes of people such as their gender, the shape of their head or the color of their hair. There is growing interest in soft biometrics as a means of improving automated face recognition since they hold the promise of significantly reducing recognition errors, in part by ruling out illogical choices. This paper concentrates specifically on soft biometrics...
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...
Since mirror-like odd and even features in face recognition reflect the symmetrical and asymmetrical image information, respectively, their proper combination can improve the recognition rates to some extent. However, the face imaging process can easily be affected by external factors and encounter the noise signal, which disturbs the effect of face recognition based on combinational mirror-like odd...
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)...
The Point-and-Shoot Face Recognition Challenge (PaSC) is a performance evaluation challenge including 1401 videos of 265 people acquired with handheld cameras and depicting people engaged in activities with non-frontal head pose. This report summarizes the results from a competition using this challenge problem. In the Video-to-video Experiment a person in a query video is recognized by comparing...
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...
Methods for assessing the impact of factors and image-quality metrics for still face images are well-understood. The extension of these factors and quality measures to faces in video has not, however, been explored. We present a specific methodology for carrying out this extension from still to video. Using the Point-and-Shoot Challenge (PaSC) dataset, our study investigates the effect of nine factors...
Inexpensive “point-and-shoot” camera technology has combined with social network technology to give the general population a motivation to use face recognition technology. Users expect a lot; they want to snap pictures, shoot videos, upload, and have their friends, family and acquaintances more-or-less automatically recognized. Despite the apparent simplicity of the problem, face recognition in this...
We investigate the existence of quality measures for face recognition. First, we introduce the concept of an oracle for image quality in the context of face recognition. Next we introduce greedy pruned ordering (GPO) as an approximation to an image quality oracle. GPO analysis provides an estimated upper bound for quality measures, given a face recognition algorithm and data set. We then assess the...
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...
We investigate the use of multiple intrinsic geometric attributes, including angles, geodesic distances, and curvatures, for 3D face recognition, where each face is represented by a triangle mesh, preprocessed to possess a uniform connectivity. As invariance to facial expressions holds the key to improving recognition performance, we propose to train for the component-wise weights to be applied to...
In this paper, a novel algorithm is proposed to locate facial feature points robustly and accurately. It uses the coarse-to-fine strategy to search feature points. Firstly, it used phase congruency to model the feature points locally and locate facial feature points roughly. Secondly, it used reflectance, which is intrinsic texture of object, to drive the search of fine location. Experiments showed...
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