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In the last decade, research in biometrics has been focused on augmenting the algorithmic performance to address a growing range of applications, not limited to person authentication/recognition. The concept of context awareness emerged as a possible key-factor for both performance optimization and operational adaptation of the capture, extraction, matching and decision stages. This may be particularly effective for multi-biometrics systems. The knowledge of the context in which a task is being performed, may provide useful information to the system in several manners. For example, it may allow to adapt to a specific environmental condition, such as shadow or light exposure. On the other hand, it may be possible to select the best available algorithm, among a given set to address the task at hand, which best performs within the given context. This paper aims to provide an overall vision of the main contributions available so far in the field of context-aware biometric systems and methods. The survey is not confined to a particular biometric modality or processing stage, but rather spans the state of the art of several biometric modalities and approaches. A taxonomy of context-aware biometric systems and methods is also proposed, along with a comparison of their features, aims and performances. The analysis will be complemented with a critical discussion about the state of the art also suggesting some future application scenarios. [ABSTRACT FROM AUTHOR]

An exponential growth of the number of plastic surgery treatments specific to face (from the minimally-invasive ones to the real surgical procedures) has characterized the last two decades and it seems likely that this phenomenon, that has social and cultural meanings and implications, could spread even further in the next years as the average cost of these treatments is lowering and the wish for “beautification” is becoming part of the global esthetics sense. For these reasons, face recognition as an established research topic has a new major challenge: delivering methods capable of high recognition accuracy even in case probe and gallery differ by a surgical alteration of face shape. To this aim is of fundamental importance understanding the range and the extent of the modification produced by the various types of treatments or by a combination of them. We present a survey of the state of the art on this topic, starting by an analysis of the diffusion of the facial plastic surgery and describing the key aspects of each of the most statistically relevant treatments available, resumed by a synthetic table. The paper includes a brief description of all the approaches proposed in the field so far to the best of authors' knowledge and a comparison of the performance reported by the existing methods when applied to the most referenced plastic surgery face dataset to date. A critical discussion of the results achieved so far and an insight about the challenges that still have to be addressed concludes this work. [ABSTRACT FROM AUTHOR]

Human-Object Interaction (HOI) detection recognizes how persons interact with objects, which is advantageous in autonomous systems such as self-driving vehicles and collaborative robots. However, current HOI detectors are often plagued by model inefficiency and unreliability when making a prediction, which consequently limits its potential for real-world scenarios. In this paper, we address these challenges by proposing ERNet, an end-to-end trainable convolutional-transformer network for HOI detection. The proposed model employs an efficient multi-scale deformable attention to effectively capture vital HOI features. We also put forward a novel detection attention module to adaptively generate semantically rich instance and interaction tokens. These tokens undergo pre-emptive detections to produce initial region and vector proposals that also serve as queries which enhances the feature refinement process in the transformer decoders. Several impactful enhancements are also applied to improve the HOI representation learning. Additionally, we utilize a predictive uncertainty estimation framework in the instance and interaction classification heads to quantify the uncertainty behind each prediction. By doing so, we can accurately and reliably predict HOIs even under challenging scenarios. Experiment results on the HICO-Det, V-COCO, and HOI-A datasets demonstrate that the proposed model achieves state-of-the-art performance in detection accuracy and training efficiency. Codes are publicly available at https://github.com/Monash-CyPhi-AI-Research-Lab/ernet. [ABSTRACT FROM AUTHOR]

Autonomous passenger counting in public transportation represents an integral part of an intelligent transportation system, as it provides vital information to improve the efficiency and resource management of a public transportation network. However, counting passengers in highly crowded scenes is a challenging task due to their random movement, diverse appearance settings and inter-object occlusions. Furthermore, state-of-the-art methods in this domain rely heavily on additional custom cameras or sensors instead of existing onboard surveillance cameras, which consequently limits the feasibility of such systems for large-scale deployment. Hence, this paper puts forward an enhanced appearance descriptor with lateral movement compensation, which addresses the difficulty in counting passengers bidirectionally in cluttered scenes. We first construct a head re-identification dataset, which is used to train an appearance descriptor. This dataset addresses the absence of a person re-identification dataset, which in turn allows for accurate tracking of passengers in cluttered scenes. Then, a novel technique of applying a fedora counting line is introduced to count the number of passengers entering and exiting a bus. This technique compensates the impact of passengers' lateral movement, which crucially increases the accuracy of bidirectional passenger counting using onboard bus surveillance cameras. In addition, a real-time implementation of the proposed method, which includes the integration of DeepStream and fedora counting line, is also presented. Experimental results on a challenging test dataset demonstrate that the proposed method outperforms benchmarked techniques with an average counting accuracy of 93.21% for entering and 96.10% for exiting public buses. Furthermore, the proposed system achieves this accuracy at an average frame rate of 16 frames per second, which represents a practical solution to a real-time application. [ABSTRACT FROM AUTHOR]

The lack of resolution of imaging systems has critically adverse impacts on the recognition and performance of biometric systems, especially in the case of long range biometrics and surveillance such as face recognition at a distance, iris recognition and gait recognition. Super-resolution, as one of the core innovations in computer vision, has been an attractive but challenging solution to address this problem in both general imaging systems and biometric systems. However, a fundamental difference exists between conventional super-resolution motivations and those required for biometrics. The former aims to enhance the visual clarity of the scene while the latter, more significantly, aims to improve the recognition accuracy of classifiers by exploiting specific characteristics of the observed biometric traits. This paper comprehensively surveys the state-of-the-art super-resolution approaches proposed for four major biometric modalities: face (2D+3D), iris, fingerprint and gait. We approach the super-resolution problem in biometrics from several different perspectives, including from the spatial and frequency domains, single and multiple input images, learning-based and reconstruction-based approaches. Especially, we highlight two special categories: feature-domain super-resolution which performs super-resolution directly on the feature space to purposely improve the recognition performance, and deep-learning super-resolution which discusses the most recent advances in deep learning for the super-resolution task. Finally, we discuss the current and open research challenges and provide recommendations into the future for the improved use of super-resolution with biometrics. [ABSTRACT FROM AUTHOR]

Most of the time, when people observe, interact or speak to each other, they focus the attention on the ocular parts of the face. This daily life experience has a strong impact on the analysis of periocular facial regions. These facial regions may be exploited in order to identify individuals for several applications, including access control and services such as telebanking and electronic transactions. In this paper we suggest studying the efficiency of the periocular regions on gender and race prediction. Most researchers propose a local texture description based on LBP (Local Binary Pattern) and HoG (Histogram of Oriented Gradients) for the purpose of predicting gender. On the other hand, Deep learning techniques were proposed to predict the gender. However, this requires a huge labeled periocular data for gender which is not available. Also, the expressivity of gender and race can be decreased on the final representation of the Deep architectures comparing to the earlier stages. To overcome these points and for the aim of predicting gender and race, considering also the high impact of DCNNs (Deep Convolutional Neural Networks) techniques to solve several aspects in biometrics, we suggest a Deep architecture based on visual attention on the periocular part. The visual saliency extraction is based on primary layers' activation by analyzing the feature-maps. We study how the visual attention-based features coupled to Deep Neural Networks can be used to discriminate between gender and race, hence extract a significant feature from periocular regions. Different pretrained architectures such as Alexnet and ResNet-50 were considered to extract visual saliency points or interest points. Several experiments were performed on periocular regions and a comparative study was conducted. The present results not only demonstrate the feasibility but also the robustness of the extracted interest points. • Efficiency of periocular region for gender and race prediction. • Deep architectures based on visual attention on periocular regions. • Visual attention-based features coupled to Deep Neural Networks. • Visual saliency extraction based on primary layers' activation. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for Human Face Recognition, namely Regularized Bi-partitioned Entropy Component Analysis (RBECA). This conservative approach regularizes the kernel entropy components by deterring the noise and affecting the lower entropy regions area, making the method robust to noise. The kernel feature space, formed by the kernel entropy component analysis (KECA), is divided into two partitions: the High Entropy Space (HES) and the Low Entropy Space (LES). The noise-laden low entropy spectrum is regularized by predicting entropy values obtained from the information-filled High Entropy Spectrum. The corresponding projection vectors are adjusted accordingly. A null space, comprising the negligible information and many dimensions, is eliminated using a Golden Search minimization function at two stages. The method retains the maximum entropy property and high recognition accuracy while using the optimum number of features. This resultant feature vector is classified using the cosine similarity measure. The algorithm is successfully tested on several benchmark databases like AR, FERET, FRAV2D, and LFW, using standard protocols and compared with other competitive methods. The proposed method achieves much better recognition accuracy than other well-known methods like PCA, ICA, KPCA, KECA, LGBP, ERE, etc., in all considered cases. Moreover, we have also proposed a CNN for the comparative analysis. For unbiased or fair performance evaluation, the sensitivity and specificity are also reported. • A noise robust RBECA method is proposed for human face recognition. • The proposed method requires a lower number of features than KECA. • The highest level of discriminatory information is retained. • The algorithm is trained and tested on several benchmark face databases. • A deep learning CNN framework is also implemented for comparative analysis. [ABSTRACT FROM AUTHOR]

Innovation has formed much of the rich history in biometrics. The field of soft biometrics was originally aimed to augment the recognition process by fusion of metrics that were sufficient to discriminate populations rather than individuals. This was later refined to use measures that could be used to discriminate individuals, especially using descriptions that can be perceived using human vision and in surveillance imagery. A further branch of this new field concerns approaches to estimate soft biometrics, either using conventional biometrics approaches or just from images alone. These three strands combine to form what is now known as soft biometrics. We survey the achievements that have been made in recognition by and in estimation of these parameters, describing how these approaches can be used and where they might lead to. The approaches lead to a new type of recognition, and one similar to Bertillonage which is one of the earliest approaches to human identification. [ABSTRACT FROM AUTHOR]