Pattern mining approaches used in sensor-based biometric recognition: a review

Sensing technologies place significant interest in the use of biometrics for the recognition and assessment of individuals. Pattern mining techniques have established a critical step in the progress of sensor-based biometric systems that are capable of perceiving, recognizing and computing sensor data, being a technology that searches for the high-level information about pattern recognition from low-level sensor readings in order to construct an artificial substitute for human recognition. The design of a successful sensor-based biometric recognition system needs to pay attention to the different issues involved in processing variable data being - acquisition of biometric data from a sensor, data pre-processing, feature extraction, recognition and/or classification, clustering and validation. A significant number of approaches from image processing, pattern identification and machine learning have been used to process sensor data. This paper aims to deliver a state-of-the-art summary and present strategies for utilizing the broadly utilized pattern mining methods in order to identify the challenges as well as future research directions of sensor-based biometric systems

On Generative Adversarial Network Based Synthetic Iris Presentation Attack And Its Detection

Human iris is considered a reliable and accurate modality for biometric recognition due to its unique texture information. Reliability and accuracy of iris biometric modality have prompted its large-scale deployment for critical applications such as border control and national identification projects. The extensive growth of iris recognition systems has raised apprehensions about the susceptibility of these systems to various presentation attacks. In this thesis, a novel iris presentation attack using deep learning based synthetically generated iris images is presented. Utilizing the generative capability of deep convolutional generative adversarial networks and iris quality metrics, a new framework, named as iDCGAN is proposed for creating realistic appearing synthetic iris images. In-depth analysis is performed using quality score distributions of real and synthetically generated iris images to understand the effectiveness of the proposed approach. We also demonstrate that synthetically generated iris images can be used to attack existing iris recognition systems. As synthetically generated iris images can be effectively deployed in iris presentation attacks, it is important to develop accurate iris presentation attack detection algorithms which can distinguish such synthetic iris images from real iris images. For this purpose, a novel structural and textural feature-based iris presentation attack detection framework (DESIST) is proposed. The key emphasis of DESIST is on developing a unified framework for detecting a medley of iris presentation attacks, including synthetic iris. Experimental evaluations showcase the efficacy of the proposed DESIST framework in detecting synthetic iris presentation attacks

Pose Invariant 3D Face Authentication based on Gaussian Fields Approach

This thesis presents a novel illuminant invariant approach to recognize the identity of an individual from his 3D facial scan in any pose, by matching it with a set of frontal models stored in the gallery. In view of today’s security concerns, 3D face reconstruction and recognition has gained a significant position in computer vision research. The non intrusive nature of facial data acquisition makes face recognition one of the most popular approaches for biometrics-based identity recognition. Depth information of a 3D face can be used to solve the problems of illumination and pose variation associated with face recognition. The proposed method makes use of 3D geometric (point sets) face representations for recognizing faces. The use of 3D point sets to represent human faces in lieu of 2D texture makes this method robust to changes in illumination and pose. The method first automatically registers facial point-sets of the probe with the gallery models through a criterion based on Gaussian force fields. The registration method defines a simple energy function, which is always differentiable and convex in a large neighborhood of the alignment parameters; allowing for the use of powerful standard optimization techniques. The new method overcomes the necessity of close initialization and converges in much less iterations as compared to the Iterative Closest Point algorithm. The use of an optimization method, the Fast Gauss Transform, allows a considerable reduction in the computational complexity of the registration algorithm. Recognition is then performed by using the robust similarity score generated by registering 3D point sets of faces. Our approach has been tested on a large database of 85 individuals with 521 scans at different poses, where the gallery and the probe images have been acquired at significantly different times. The results show the potential of our approach toward a fully pose and illumination invariant system. Our method can be successfully used as a potential biometric system in various applications such as mug shot matching, user verification and access control, and enhanced human computer interaction

Speech Processing in Computer Vision Applications

Deep learning has been recently proven to be a viable asset in determining features in the field of Speech Analysis. Deep learning methods like Convolutional Neural Networks facilitate the expansion of specific feature information in waveforms, allowing networks to create more feature dense representations of data. Our work attempts to address the problem of re-creating a face given a speaker\u27s voice and speaker identification using deep learning methods. In this work, we first review the fundamental background in speech processing and its related applications. Then we introduce novel deep learning-based methods to speech feature analysis. Finally, we will present our deep learning approaches to speaker identification and speech to face synthesis. The presented method can convert a speaker audio sample to an image of their predicted face. This framework is composed of several chained together networks, each with an essential step in the conversion process. These include Audio embedding, encoding, and face generation networks, respectively. Our experiments show that certain features can map to the face and that with a speaker\u27s voice, DNNs can create their face and that a GUI could be used in conjunction to display a speaker recognition network\u27s data

Development and Performance Evaluation of Hausdorff Distance Algorithm Based Facial Recognition System

Securing access to information is of primary concern in many frame of reference including personal, commercial, governmental and military purpose. Computer verifiable biometric such as face provide an attractive means of securing access to information. Earlier algorithms for facial recognition system which includes Linear Discriminant Analysis (LDA), Principle Component Analysis (PCA) andIndependent Component Analysis (ICA) have yielded unsatisfactory result especially when confronted with unconstrained scenarios such as varying illumination, varying poses, expression and aging. This work presents a facial recognition authentication system using hausdorff distance algorithm in combating the highlighted problems. A system camera was employed for capturing images, information was stored using MYSQL database and biometric templates were stored as binary large object (BLOB). The developed system performance was evaluated using False Reject Rate (FRR), False Accept Rate (FAR), and Receiver Operating Characteristic Curve (ROC graph) as performance metrics. Tests were conducted at various threshold values. FRR errors obtained are 20%, 7%, and 2% at 500 threshold value for one-try, two-try and three-try configuration respectively. The system also presented FAR error of 0% at 500 threshold value for all configurations. As threshold value increases, FAR reduces while FRR increases

Edge AI on a Deep-Learning based Real-Time Face Identification and Attributes Recognition System

There is another way of understanding how a customer service office works, and Everis is developing it in its new generation of spaces designed to offer easy and personalized attention to its customers. Some of the technologies implemented in this space to offer a better experience range from voice recognition or facial identification to the detection of hand gestures. The purpose of the project is to incorporate into the Everis customer e-Motion HUB a new computer vision-based system to extend its abilities and to improve the user experience.Face recognition systems are nowadays being used in a variety of settings, including surveillance systems and human-computer interactions. Different approaches have been used for face recognition throughout the years, but recent research has shown that Deep Learning models along with Convolutional Neural Networks, or \gls{CNN}s, provide better results than any other methods. However, these more complex \gls{CNN} models have several limitations, including the need for extensive training data or high computational requirements in some cases. Fields such as robotics and embedded systems that deploy face recognition systems have significantly less power on board and limited heat dissipation capacity. Therefore, it can be difficult to deploy deep learning models on them. Additionally, and to counter these issues, the classical approach in some industries has been to rely on cloud computing or other third companies paid services. Edge computing devices, such as the NVIDIA Jetson Nano proposed in this approach, can bridge this gap by providing certain advantages in many different areas. In this thesis, we explore the Edge Artificial Intelligence or Edge AI capabilities by developing and implementing a real-time face recognition system along with multiple feature extraction namely age, gender, emotions, and paid attention. Additionally, we provide a data storing approach into a relational database so that all the gathered information can be further exploited. Although this work has certain areas that can be improved, mainly with regards to its efficiency, it has served as a proof of concept for the ideas behind it. Consequently, research in this direction will surely be continued