A survey on facial soft biometrics for video surveillance and forensic applications

Study of image sensors for enhanced face recognition at a distance in the Smart City context

Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.

ApaNet: adversarial perturbations alleviation network for face verification

Albeit Deep neural networks (DNNs) are widely used in computer vision, natural language processing and speech recognition, they have been discovered to be fragile to adversarial attacks. Specifically, in computer vision, an attacker can easily deceive DNNs by contaminating an input image with perturbations imperceptible to humans. As one of the important vision tasks, face verification is also subject to adversarial attack. Thus, in this paper, we focus on defending against the adversarial attack for face verification to mitigate the potential risk. We learn a network via an implementation of stacked residual blocks, namely adversarial perturbations alleviation network (ApaNet), to alleviate latent adversarial perturbations hidden in the input facial image. During the supervised learning of ApaNet, only the Labeled Faces in the Wild (LFW) is used as the training set, and the legitimate examples and corresponding adversarial examples produced by projected gradient descent algorithm compose supervision and inputs respectively. By leveraging the middle and high layer's activation of FaceNet, the discrepancy between an image output by ApaNet and the supervision is calculated as the loss function to optimize ApaNet. Empirical experiment results on the LFW, YouTube Faces DB and CASIA-FaceV5 confirm the effectiveness of the proposed defender against some representative white-box and black-box adversarial attacks. Also, experimental results show the superiority performance of the ApaNet as comparing with several currently available techniques.

A Method for Human Facial Image Annotation on Low Power Consumption Autonomous Devices

This paper proposes a classifier designed for human facial feature annotation, which is capable of running on relatively cheap, low power consumption autonomous microcomputer systems. An autonomous system is one that depends only on locally available hardware and software—for example, it does not use remote services available through the Internet. The proposed solution, which consists of a Histogram of Oriented Gradients (HOG) face detector and a set of neural networks, has comparable average accuracy and average true positive and true negative ratio to state-of-the-art deep neural network (DNN) architectures. However, contrary to DNNs, it is possible to easily implement the proposed method in a microcomputer with very limited RAM memory and without the use of additional coprocessors. The proposed method was trained and evaluated on a large 200,000 image face data set and compared with results obtained by other researchers. Further evaluation proves that it is possible to perform facial image attribute classification using the proposed algorithm on incoming video data captured by an RGB camera sensor of the microcomputer. The obtained results can be easily reproduced, as both the data set and source code can be downloaded. Developing and evaluating the proposed facial image annotation algorithm and its implementation, which is easily portable between various hardware and operating systems (virtually the same code works both on high-end PCs and microcomputers using the Windows and Linux platforms) and which is dedicated for low power consumption devices without coprocessors, is the main and novel contribution of this research.