TL;DR: In this paper, a modified high frequency descriptor is used for proper discrimination between a live and fake facial video streams, which works efficiently for a change in facial micro-expression (μE) in higher frequency spectrum.
Abstract: Facial replay attacks have been a topic of interest in recent past due to the vulnerability of intrusive nature in biometric security systems In order to build a robust biometric system many safeguard approaches have already been developed by the researchers to nullify spoofing activities like print and replay attacks This paper proposes a comprehensive study on the application of Multidimensional Fourier transform to combat replay attacks Since the higher frequency in Multidimensional Fourier transform contains the major feature variations, liveness of a face is mostly reflected in the high frequency spectrum The spontaneous facial expressions like micro-expression( μ E) carries the detailed inner facial variations In this novel approach a modified high frequency descriptor is used for proper discrimination between a live and fake facial video streams The descriptor in particular works efficiently for a change in facial μ E Inclusion of noise along with the feature variation is trivial in higher frequency spectrum The method, therefore, during the pre-processing phase not only extracts the video frames with major μ E changes but also filters out frames carrying any abrupt expression change (macro expression) or spike noise The selected frame sequence are thereafter fed into the multi dimensional Fourier plane in order to detect the liveness The experiment is performed on the self created dataset and also being tested on standard play back attack dataset The result obtained by the proposed anti spoofing approach is satisfactory and verified to be statistically significant
TL;DR: In this article, multi-spectral satellite remote sensing imagery have several applications including detection of objects or distinguishing land surface areas based on amount of greenery or water etc. The enhanceme...
Abstract: Multi-spectral satellite remote sensing imagery have several applications including detection of objects or distinguishing land surface areas based on amount of greenery or water etc. The enhanceme...
TL;DR: In this work, it is found that there are some limitations during the training process, in particular, an imbalance in the distribution of motion amplitudes of samples, optical flow features, and semantic features, so adaptive balanced magnification, the balance of Optical flow features and the Balance of enhanced semantic features is proposed to reduce these imbalances.
Abstract: Micro-expressions are subtle facial movements that expose a person’s hidden emotions. Recognizing the micro-expression has importance for example in criminal investigations and psychotherapy. Compared with the shallower-architecture model, image magnification of these movements, which is also crucial for accurate recognition, has received relatively less attention in the field of micro-expression recognition. In this work, we find that there are some limitations during the training process, in particular, an imbalance in the distribution of motion amplitudes of samples, optical flow features, and semantic features. To mitigate their adverse effects, we propose adaptive balanced magnification, the balance of optical flow features and the balance of enhanced semantic features, to reduce these imbalances. Experimental results from three benchmarks (CASMEII, SAMM, and SMIC) show that our proposed method has higher accuracy and better recognition success than other micro-expression recognition methods.
TL;DR: In this article , a systematic survey of face anti-spoofing with prognostic trends in this research area is presented, where five types of generalization such as transfer learning, anomaly detection, few-shot and zero-shot learning, auxiliary supervision, and multi-spectral methods.
Abstract: The rapid development of biometric methods and their implementation in practice has led to the widespread attacks called spoofing, which are purely biometric vulnerabilities, but are not used in conjunction with other IT security solutions. Although biometric recognition as a branch of computer science dates back to the 1960s, attacks on biometric systems have become more sophisticated since the 2010s due to great advances in pattern recognition. It should be noted that face recognition is the most attractive topic for deceiving recognition systems. Popular presentation attacks, such as print, replay and mask attacks, have demonstrated a high security risk for SOTA face recognition systems. Many Presentation Attack Detection (PAD) methods (also known as face anti-spoofing methods or countermeasures) have been proposed that can automatically detect and mitigate such targeted attacks. The article presents a systematic survey in face anti-spoofing with prognostic trends in this research area. A brief description of 16 outstanding previous surveys on the face PAD field is mentioned, from which it is possible to trace how this scientific topic has developed. SOTA in PAD provides an analysis of a wide range of the PAD methods, which are categorized into two unbalanced groups: digital (feature-based) and physical (sensor-based) methods. Generalization of deep learning methods as a recent trend aimed at improving recognition results requires special attention. This survey presents five types of generalization such as transfer learning, anomaly detection, few-shot and zero-shot learning, auxiliary supervision, and multi-spectral methods. A summary of over than 40 existing 2D/3D face spoofing databases is a guideline for those who want to select databases for experiments. One can also find a description of performance evaluation metrics and testing protocols. In addition, we discuss trends and perspectives in the emerging field of facial biometrics.