Two-Dimensional Face Recognition Algorithms in the Frequency Domain

ABSTRACT Two-Dimensional Face Recognition Algorithms in the Frequency Domain Alper Serhat Zeytunlu The importance of security, law-enforcement and identity verification has necessitated the development of automated stable, fast and highly accurate algorithms for human recognition. Face recognition is one of the most popular techniques used for these purposes. Face recognition algorithms are performed on very large size of datasets obtained under various challenging conditions. Principal component analysis (PCA) is a widely used technique for face recognition. However, it has major drawbacks of (i) losing the image details due to the transformation of two-dimensional face images into one-dimensional vectors, (ii) having a large time complexity due to the use of a large size covariance matrix and (iii) suffering from the adverse effect of intra-class pose variations resulting in reduced recognition accuracy. To overcome the problem of intra-class pose variations, Fourier magnitudes have been used for feature extraction in the PCA algorithm giving rise to the so called FM-PCA algorithm. However, the time complexity of this algorithm is even higher. On the other hand, to address the other two drawbacks of the PCA algorithm, two-dimensional PCA (2DPCA) algorithms have been proposed. This thesis is concerned with developing 2DPCA algorithms that incorporate the advantages of FM-PCA in improving the accuracy and that of 2DPCA algorithms in improving the accuracy as well as the time complexity. Towards this goal, 2DPCA algorithms, referred to as the FM-r2DPCA and FM-(2D)2PCA algorithms, that use Fourier-magnitudes rather than the raw pixel values, are first developed. Extensive simulations are conducted to demonstrate the effectiveness of using the Fourier-magnitudes in providing higher recognition accuracy over their spatial domain counterparts. Next, by taking advantage of the energy compaction property of the Fourier-magnitudes, the proposed algorithms are further developed to significantly reduce their computational complexities with little loss in the recognition accuracy. Simulation results are provided to validate this claim

Automatic Face Recognition System Based on Local Fourier-Bessel Features

We present an automatic face verification system inspired by known properties of biological systems. In the proposed algorithm the whole image is converted from the spatial to polar frequency domain by a Fourier-Bessel Transform (FBT). Using the whole image is compared to the case where only face image regions (local analysis) are considered. The resulting representations are embedded in a dissimilarity space, where each image is represented by its distance to all the other images, and a Pseudo-Fisher discriminator is built. Verification test results on the FERET database showed that the local-based algorithm outperforms the global-FBT version. The local-FBT algorithm performed as state-of-the-art methods under different testing conditions, indicating that the proposed system is highly robust for expression, age, and illumination variations. We also evaluated the performance of the proposed system under strong occlusion conditions and found that it is highly robust for up to 50% of face occlusion. Finally, we automated completely the verification system by implementing face and eye detection algorithms. Under this condition, the local approach was only slightly superior to the global approach.Comment: 2005, Brazilian Symposium on Computer Graphics and Image Processing, 18 (SIBGRAPI

Face image super-resolution using 2D CCA

In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches. © 2013 Elsevier B.V

A statistical multiresolution approach for face recognition using structural hidden Markov models

This paper introduces a novel methodology that combines the multiresolution feature of the discrete wavelet transform (DWT) with the local interactions of the facial structures expressed through the structural hidden Markov model (SHMM). A range of wavelet filters such as Haar, biorthogonal 9/7, and Coiflet, as well as Gabor, have been implemented in order to search for the best performance. SHMMs perform a thorough probabilistic analysis of any sequential pattern by revealing both its inner and outer structures simultaneously. Unlike traditional HMMs, the SHMMs do not perform the state conditional independence of the visible observation sequence assumption. This is achieved via the concept of local structures introduced by the SHMMs. Therefore, the long-range dependency problem inherent to traditional HMMs has been drastically reduced. SHMMs have not previously been applied to the problem of face identification. The results reported in this application have shown that SHMM outperforms the traditional hidden Markov model with a 73% increase in accuracy