TEXTURE MODELING AND SIMULATION FOR SYNTHETIC PALM VEIN IMAGE GENERATION SYSTEM

Unavailability of large-scale palm vein databases due to their intrusiveness have posed challenges in exploring this technology for large-scale applications. Hence, this research modelled and generated synthetic palm vein images from only a couple of initial samples using statistical features. Variations were introduced to the three optimized statistical features (S5; the original images were employed as training images and the best variation in the first experiment  as training images, S4; the best variation in the first experiment  as training images while the original images were used as testing images, S3; mean vectors, covariance matrices and correlation coefficient, S2; mean vectors and covariance matrices, S1; mean vectors, Non-Synthetic; acquired image) which were used to generate synthetic palm vein images employing statistical and Genetic Algorithm (GA) approaches and were evaluated based on Equal Error Rate (EER), Average Recognition Accuracy (ARA) and Average Recognition Time (ART). The results obtained from the experiment showed that EERs were 0.22, 0.51, 0.58 and 4.36 for S3, S2, S1 and NS respectively. S3 had superior ARA (99.83%) compared with S2 (99.77 %), S1 (99.70 %) and NS (98.33 %). The ARTs obtained were 84.97s, 75.55s, 84.04s and 681.74s for S1, S2, S3 and NS respectively with S2 (75.55s) having significantly least value. Furthermore, EER, ARA and ART for S4 were 0.43, 99.00%, and 12.13s, respectively while the corresponding values for S5 were 1.43, 97.50%, and 680.13s, respectively. The research outcome justifies the extraction of mean vectors, covariance matrices and correlation coefficient

Deep Multi-task Multi-label CNN for Effective Facial Attribute Classification

Facial Attribute Classification (FAC) has attracted increasing attention in computer vision and pattern recognition. However, state-of-the-art FAC methods perform face detection/alignment and FAC independently. The inherent dependencies between these tasks are not fully exploited. In addition, most methods predict all facial attributes using the same CNN network architecture, which ignores the different learning complexities of facial attributes. To address the above problems, we propose a novel deep multi-task multi-label CNN, termed DMM-CNN, for effective FAC. Specifically, DMM-CNN jointly optimizes two closely-related tasks (i.e., facial landmark detection and FAC) to improve the performance of FAC by taking advantage of multi-task learning. To deal with the diverse learning complexities of facial attributes, we divide the attributes into two groups: objective attributes and subjective attributes. Two different network architectures are respectively designed to extract features for two groups of attributes, and a novel dynamic weighting scheme is proposed to automatically assign the loss weight to each facial attribute during training. Furthermore, an adaptive thresholding strategy is developed to effectively alleviate the problem of class imbalance for multi-label learning. Experimental results on the challenging CelebA and LFWA datasets show the superiority of the proposed DMM-CNN method compared with several state-of-the-art FAC methods

Spartan Face Mask Detection and Facial Recognition System

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection and Facial Recognition System with stacking ensemble deep learning algorithms is proposed to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms used to classify the features in each scenario. This system is powered by five components including training platform, server, supporting frameworks, hardware, and user interface. Complete unit tests, use cases, and results analytics are used to evaluate and monitor the performance of the system. The system provides cost-efficient face detection and facial recognition with masks solutions for enterprises and schools that can be easily applied on edge-devices

Multimodal Adversarial Learning

Deep Convolutional Neural Networks (DCNN) have proven to be an exceptional tool for object recognition, generative modelling, and multi-modal learning in various computer vision applications. However, recent findings have shown that such state-of-the-art models can be easily deceived by inserting slight imperceptible perturbations to key pixels in the input. A good target detection systems can accurately identify targets by localizing their coordinates on the input image of interest. This is ideally achieved by labeling each pixel in an image as a background or a potential target pixel. However, prior research still confirms that such state of the art targets models are susceptible to adversarial attacks. In the case of generative models, facial sketches drawn by artists mostly used by law enforcement agencies depend on the ability of the artist to clearly replicate all the key facial features that aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. However, the incorporation of hybrid discriminators, which perform attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network have shown to be powerful tools towards better multi modal learning techniques. In general, our overall approach was aimed at improving target detection systems and the visual appeal of synthesized images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesized image. We synthesized sketches using XDOG filter for the CelebA, Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results overall for different model applications are impressive compared to current state of the art

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area

Handbook of Digital Face Manipulation and Detection

This open access book provides the first comprehensive collection of studies dealing with the hot topic of digital face manipulation such as DeepFakes, Face Morphing, or Reenactment. It combines the research fields of biometrics and media forensics including contributions from academia and industry. Appealing to a broad readership, introductory chapters provide a comprehensive overview of the topic, which address readers wishing to gain a brief overview of the state-of-the-art. Subsequent chapters, which delve deeper into various research challenges, are oriented towards advanced readers. Moreover, the book provides a good starting point for young researchers as well as a reference guide pointing at further literature. Hence, the primary readership is academic institutions and industry currently involved in digital face manipulation and detection. The book could easily be used as a recommended text for courses in image processing, machine learning, media forensics, biometrics, and the general security area