Biometric face recognition using multilinear projection and artificial intelligence

PhD ThesisNumerous problems of automatic facial recognition in the linear and multilinear subspace learning have been addressed; nevertheless, many difficulties remain. This work focuses on two key problems for automatic facial recognition and feature extraction: object representation and high dimensionality. To address these problems, a bidirectional two-dimensional neighborhood preserving projection (B2DNPP) approach for human facial recognition has been developed. Compared with 2DNPP, the proposed method operates on 2-D facial images and performs reductions on the directions of both rows and columns of images. Furthermore, it has the ability to reveal variations between these directions. To further improve the performance of the B2DNPP method, a new B2DNPP based on the curvelet decomposition of human facial images is introduced. The curvelet multi- resolution tool enhances the edges representation and other singularities along curves, and thus improves directional features. In this method, an extreme learning machine (ELM) classifier is used which significantly improves classification rate. The proposed C-B2DNPP method decreases error rate from 5.9% to 3.5%, from 3.7% to 2.0% and from 19.7% to 14.2% using ORL, AR, and FERET databases compared with 2DNPP. Therefore, it achieves decreases in error rate more than 40%, 45%, and 27% respectively with the ORL, AR, and FERET databases. Facial images have particular natural structures in the form of two-, three-, or even higher-order tensors. Therefore, a novel method of supervised and unsupervised multilinear neighborhood preserving projection (MNPP) is proposed for face recognition. This allows the natural representation of multidimensional images 2-D, 3-D or higher-order tensors and extracts useful information directly from tensotial data rather than from matrices or vectors. As opposed to a B2DNPP which derives only two subspaces, in the MNPP method multiple interrelated subspaces are obtained over different tensor directions, so that the subspaces are learned iteratively by unfolding the tensor along the different directions. The performance of the MNPP has performed in terms of the two modes of facial recognition biometrics systems of identification and verification. The proposed supervised MNPP method achieved decrease over 50.8%, 75.6%, and 44.6% in error rate using ORL, AR, and FERET databases respectively, compared with 2DNPP. Therefore, the results demonstrate that the MNPP approach obtains the best overall performance in various learning scenarios

Face Recognition Using Holistic Features and Within Class Scatter-Based PCA

The Principle Component Analysis (PCA) and itsvariations are the most popular approach for features clustering,which is mostly implemented for face recognition. The optimumprojection matrix of the PCA is typically obtained by eigenanalysisof global covariance matrix. However, the projection datausing the PCA are lack of discriminatory power. This problem iscaused by removing the null space of data scatter that containsmuch discriminant information. To solve this problem, we presentalternative strategy to the PCA called alternative PCA, whichobtains the optimum projection matrix from within class scatterinstead of global covariance matrix. This algorithm not onlyprovides better features clustering than that of common PCA(CPCA) but also can overcome the retraining problem of theCPCA. In this paper, this algorithm is applied for face recognitionwith the holistic features of face image, which has compact sizeand powerful energy compactness as dimensional reduction ofthe raw face image. From the experimental results, the proposedmethod provides better performance for both recognition rateand accuracy parameters than those of CPCA and its variationswhen the tests were carried out using data from several databasessuch as ITS-LAB., INDIA, ORL, and FERET

Palmprint Recognition Using Different Level of Information Fusion

The aim of this paper is to investigate a fusion approach suitable for palmprint recognition. Several number of fusion stageis analyse such as feature, matching and decision level. Fusion at feature level is able to increase discrimination power in the feature space by producing high dimensional fuse feature vector. Fusion at matching score level utilizes the matching output from different classifier to form a single value for decision process. Fusion at decision level on the other hand utilizes minimal information from a different matching process and the integration at this stage is less complex compare to other approach. The analysis shows integration at feature level produce the best recognition rates compare to the other method

Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling

Local binary pattern (LBP) algorithm and its variants have been used extensively to analyse the local textural features of digital images with great success. Numerous extensions of LBP descriptors have been suggested, focusing on improving their robustness to noise and changes in image conditions. In our research, inspired by the concepts of LBP feature descriptors and a random sampling subspace, we propose an ensemble learning framework, using a variant of LBP constructed from Pascal’s coefficients of n-order and referred to as a multiscale local binary pattern. To address the inherent overfitting problem of linear discriminant analysis, PCA was applied to the training samples. Random sampling was used to generate multiple feature subsets. In addition, in this work, we propose a new feature extraction technique that combines the pyramid histogram of oriented gradients and LBP, where the features are concatenated for use in the classification. Its performance in recognition was evaluated using the Hong Kong Polytechnic University database. Extensive experiments unmistakably show the superiority of the proposed approach compared to state-of-the-art techniques

HB-net: Holistic bursting cell cluster integrated network for occluded multi-objects recognition

Within the realm of image recognition, a specific category of multi-label classification (MLC) challenges arises when objects within the visual field may occlude one another, demanding simultaneous identification of both occluded and occluding objects. Traditional convolutional neural networks (CNNs) can tackle these challenges; however, those models tend to be bulky and can only attain modest levels of accuracy. Leveraging insights from cutting-edge neural science research, specifically the Holistic Bursting (HB) cell, this paper introduces a pioneering integrated network framework named HB-net. Built upon the foundation of HB cell clusters, HB-net is designed to address the intricate task of simultaneously recognizing multiple occluded objects within images. Various Bursting cell cluster structures are introduced, complemented by an evidence accumulation mechanism. Testing is conducted on multiple datasets comprising digits and letters. The results demonstrate that models incorporating the HB framework exhibit a significant $2.98\%$ enhancement in recognition accuracy compared to models without the HB framework ($1.0298$ times, $p=0.0499$). Although in high-noise settings, standard CNNs exhibit slightly greater robustness when compared to HB-net models, the models that combine the HB framework and EA mechanism achieve a comparable level of accuracy and resilience to ResNet50, despite having only three convolutional layers and approximately $1/30$ of the parameters. The findings of this study offer valuable insights for improving computer vision algorithms. The essential code is provided at https://github.com/d-lab438/hb-net.git

RPG-Palm: Realistic Pseudo-data Generation for Palmprint Recognition

Palmprint recently shows great potential in recognition applications as it is a privacy-friendly and stable biometric. However, the lack of large-scale public palmprint datasets limits further research and development of palmprint recognition. In this paper, we propose a novel realistic pseudo-palmprint generation (RPG) model to synthesize palmprints with massive identities. We first introduce a conditional modulation generator to improve the intra-class diversity. Then an identity-aware loss is proposed to ensure identity consistency against unpaired training. We further improve the B\'ezier palm creases generation strategy to guarantee identity independence. Extensive experimental results demonstrate that synthetic pretraining significantly boosts the recognition model performance. For example, our model improves the state-of-the-art B\'ezierPalm by more than $5\%$ and $14\%$ in terms of TAR@FAR=1e-6 under the $1:1$ and $1:3$ Open-set protocol. When accessing only $10\%$ of the real training data, our method still outperforms ArcFace with $100\%$ real training data, indicating that we are closer to real-data-free palmprint recognition.Comment: 12 pages,8 figure