State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Temporal Model Adaptation for Person Re-Identification

Person re-identification is an open and challenging problem in computer vision. Majority of the efforts have been spent either to design the best feature representation or to learn the optimal matching metric. Most approaches have neglected the problem of adapting the selected features or the learned model over time. To address such a problem, we propose a temporal model adaptation scheme with human in the loop. We first introduce a similarity-dissimilarity learning method which can be trained in an incremental fashion by means of a stochastic alternating directions methods of multipliers optimization procedure. Then, to achieve temporal adaptation with limited human effort, we exploit a graph-based approach to present the user only the most informative probe-gallery matches that should be used to update the model. Results on three datasets have shown that our approach performs on par or even better than state-of-the-art approaches while reducing the manual pairwise labeling effort by about 80%

Face recognition using statistical adapted local binary patterns.

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images

FACE RECOGNITION AND VERIFICATION IN UNCONSTRAINED ENVIRIONMENTS

Face recognition has been a long standing problem in computer vision. General face recognition is challenging because of large appearance variability due to factors including pose, ambient lighting, expression, size of the face, age, and distance from the camera, etc. There are very accurate techniques to perform face recognition in controlled environments, especially when large numbers of samples are available for each face (individual). However, face identification under uncontrolled( unconstrained) environments or with limited training data is still an unsolved problem. There are two face recognition tasks: face identification (who is who in a probe face set, given a gallery face set) and face verification (same or not, given two faces). In this work, we study both face identification and verification in unconstrained environments. Firstly, we propose a face verification framework that combines Partial Least Squares (PLS) and the One-Shot similarity model[1]. The idea is to describe a face with a large feature set combining shape, texture and color information. PLS regression is applied to perform multi-channel feature weighting on this large feature set. Finally the PLS regression is used to compute the similarity score of an image pair by One-Shot learning (using a fixed negative set). Secondly, we study face identification with image sets, where the gallery and probe are sets of face images of an individual. We model a face set by its covariance matrix (COV) which is a natural 2nd-order statistic of a sample set.By exploring an efficient metric for the SPD matrices, i.e., Log-Euclidean Distance (LED), we derive a kernel function that explicitly maps the covariance matrix from the Riemannian manifold to Euclidean space. Then, discriminative learning is performed on the COV manifold: the learning aims to maximize the between-class COV distance and minimize the within-class COV distance. Sparse representation and dictionary learning have been widely used in face recognition, especially when large numbers of samples are available for each face (individual). Sparse coding is promising since it provides a more stable and discriminative face representation. In the last part of our work, we explore sparse coding and dictionary learning for face verification application. More specifically, in one approach, we apply sparse representations to face verification in two ways via a fix reference set as dictionary. In the other approach, we propose a dictionary learning framework with explicit pairwise constraints, which unifies the discriminative dictionary learning for pair matching (face verification) and classification (face recognition) problems

Statistical methods for fine-grained retail product recognition

In recent years, computer vision has become a major instrument in automating retail processes with emerging smart applications such as shopper assistance, visual product search (e.g., Google Lens), no-checkout stores (e.g., Amazon Go), real-time inventory tracking, out-of-stock detection, and shelf execution. At the core of these applications lies the problem of product recognition, which poses a variety of new challenges in contrast to generic object recognition. Product recognition is a special instance of fine-grained classification. Considering the sheer diversity of packaged goods in a typical hypermarket, we are confronted with up to tens of thousands of classes, which, particularly if under the same product brand, tend to have only minute visual differences in shape, packaging texture, metric size, etc., making them very difficult to discriminate from one another. Another challenge is the limited number of available datasets, which either have only a few training examples per class that are taken under ideal studio conditions, hence requiring cross-dataset generalization, or are captured from the shelf in an actual retail environment and thus suffer from issues like blur, low resolution, occlusions, unexpected backgrounds, etc. Thus, an effective product classification system requires substantially more information in addition to the knowledge obtained from product images alone. In this thesis, we propose statistical methods for a fine-grained retail product recognition. In our first framework, we propose a novel context-aware hybrid classification system for the fine-grained retail product recognition problem. In the second framework, state-of-the-art convolutional neural networks are explored and adapted to fine-grained recognition of products. The third framework, which is the most significant contribution of this thesis, presents a new approach for fine-grained classification of retail products that learns and exploits statistical context information about likely product arrangements on shelves, incorporates visual hierarchies across brands, and returns recognition results as "confidence sets" that are guaranteed to contain the true class at a given confidence leve

Multi-View Face Recognition From Single RGBD Models of the Faces

This work takes important steps towards solving the following problem of current interest: Assuming that each individual in a population can be modeled by a single frontal RGBD face image, is it possible to carry out face recognition for such a population using multiple 2D images captured from arbitrary viewpoints? Although the general problem as stated above is extremely challenging, it encompasses subproblems that can be addressed today. The subproblems addressed in this work relate to: (1) Generating a large set of viewpoint dependent face images from a single RGBD frontal image for each individual; (2) using hierarchical approaches based on view-partitioned subspaces to represent the training data; and (3) based on these hierarchical approaches, using a weighted voting algorithm to integrate the evidence collected from multiple images of the same face as recorded from different viewpoints. We evaluate our methods on three datasets: a dataset of 10 people that we created and two publicly available datasets which include a total of 48 people. In addition to providing important insights into the nature of this problem, our results show that we are able to successfully recognize faces with accuracies of 95% or higher, outperforming existing state-of-the-art face recognition approaches based on deep convolutional neural networks

Review of Person Re-identification Techniques

Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201