Reliable person recognition is important for secure access and commercial applications requiring human identification.
Face recognition (FR) is an important technology being developed for human identification. Algorithms and systems for
large population face recognition (LPFR) are of significant interest in applications such as watch lists and video
surveillance. In this paper, we present correlation filter-based feature analysis methods that effectively exploit available
generic training data to represent a large number of subjects and thus improve the performance for LPFR. We first
introduce a general framework - class-dependence feature analysis (CFA), which uses correlation filters to provide a
discriminant feature representation for LPFR. We then introduce two variants of the correlation filter-based CFA
methods: 1) the kernel correlation filter CFA (KCFA) that generates nonlinear decision boundaries and significantly
improves the recognition performance without greatly increasing the computational load, and 2) the binary coding CFA
that uses binary coding to reduce the number of correlation filters and applies error control coding (ECC) to improve the
recognition performance. These two variants offer ways to tradeoff between the computational complexity and the
recognition accuracy of the CFA methods. We test our proposed algorithms on the face recognition grand challenge
(FRGC) database and show that the correlation filter-based CFA approach improves the recognition rate and reduces the
computational load over the conventional correlation filters.
The Face Recognition Grand Challenge (FRGC) dataset is one of the most challenging datasets in the face recognition community, in this dataset we focus on the hardest experiment under the harsh un-controlled conditions. In this paper we compare how other popular face recognition algorithms like Direct Linear Discriminant Analysis (D-LDA) and Gram-Schmidt LDA methods compare to traditional eigenfaces, and fisherfaces. However, we also show that all these linear subspace methods can not discriminate faces well due to large nonlinear distortions in the face images. Thus we present our proposed Class dependence Feature Analysis (CFA) method which we demonstrate to produce superior performance compared to other methods by representing nonlinear features well. We perform this by extending the traditional CFA framework to use Kernel Methods and propose a proper choice of kernel parameters which improves the overall face recognition performance is significantly over the competing face recognition algorithms. We present results of this proposed approach on a large scale database from the Face Recognition Grand Challenge (FRGC)v2 which contains over 36,000 images focusing on Experiment 4 which poses the harshest scenario containing images captured under un-controlled indoor and outdoor conditions yielding significant illumination variations.
Human face recognition is currently a very active research area with focus on ways to perform robust biometric identification. Many face recognition algorithms have been proposed, among the different approaches, frequency domain methods, like advanced correlation filters have been shown to exhibit better tolerance to illumination variations than traditional methods. In this paper, we propose a new frequency domain face recognition method which combines the Gabor transforms and a quaternion correlation filter for face recognition when the illumination conditions are changed. The Gabor transform provides optimally localized spatial and frequency domain representation of the original face images, and the quaternion correlation filters can jointly process multi-channel subbands for more robust face recognition. The numerical experiments show that the proposed method outperforms the previously compared advanced correlation filter methods.
Face image recognition has been an active research topic for many years, and many algorithms have been proposed for the grayscale face images. However, only a few methods are devoted to color face images, even though most cameras produce color images. Color can be important for face recognition. Among the proposed color face recognition methods, most treat the three color channels separately and apply some grayscale face recognition methods to each of the channels and combine the results. In this paper we propose the quaternion correlation filter techniques for color face recognition by processing all color channels jointly. Quaternion numbers are the generalization of the complex numbers and can be considered as a number with a real part and three orthogonal imaginary parts. A color image (e.g., in RGB) may be represented using quaternion by encoding the three color components to the imaginary parts of the quaternion number. The quaternion correlation filter is extended from the standard correlation filter based on the recently developed concepts of quaternion Fourier transform (QFT), quaternion convolution (QCV), and quaternion correlation (QC). Face recognition is performed by cross-correlating (QC) an input face image with a designed filter. Numerical results show that quaternion correlation filters can improve the recognition performance over conventional face recognition methods.
Face image is an attractive biometric for person verification and identification because face images can be obtained non-intrusively, even without the knowledge of the subject in some cases. But in the case that the subject is non-cooperative (in the sense of providing a predefined view for testing), the still-to-still face verification method may have difficulty in matching face images from two different view angles. In this paper we propose a still-to-video face verification method based on the optimal tradeoff synthetic discriminant function (OTSDF) filter technology for the scenario where the video sequence of a subject is available for testing. The system consists of face detection and tracking component, frame level face matching component and the evidence accumulation component. We also investigate the part-based correlation method.
3D target recognition is of significant interest because representing the object in 3D space couuld essentially provide a solution to pose variation and self-occlusion problems that are big challenges in 2D pattern recognition. Correlation filers have been used in a variety of 2D pattern matching applications and many correlation filter designs have been developed to handle problems such as rotations. Correlation filters also offer other benefits such as shift-invariance, graceful degradation and closed-form solutions. The 3D extension of correlation filter is a natural extension to handle 3D pattern recognition problem. In this paper, we propose a 3D correlation filter design method based on cylindrical circular harmonic function (CCHF) and use LADAR imagery to illustrate the good performance of CCHF filters.
Biometric verification refers to the process of matching an input biometric to stored biometric information. In particular, biometric verification refers to matching the live biometric input from an individual to the stored biometric template of that individual. Examples of biometrics include face images, fingerprint images, iris images, retinal scans, etc. Thus, image processing techniques prove useful in biometric recognition. In particular, composite correlation filters have proven to be effective. In this paper, we will discuss the application of composite correlation filters to biometric verification.
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