Real-time 3D Face Recognition using Line Projection and Mesh Sampling

The main contribution of this paper is to present a novel method for automatic 3D face recognition based on sampling a 3D mesh structure in the presence of noise. A structured light method using line projection is employed where a 3D face is reconstructed from a single 2D shot. The process from image acquisition to recognition is described with focus on its real-time operation. Recognition results are presented and it is demonstrated that it can perform recognition in just over one second per subject in continuous operation mode and thus, suitable for real time operation

Elliptical higher-order-spectra periocular code

The periocular region has recently emerged as a standalone biometric trait, promising attractive trade-off between the iris alone and the entire face, especially for cases where neither the iris nor a full facial image can be acquired. This advantage provides another dimension for implementing a robust biometric system, performed in non-ideal conditions. Global features (LBP, HOG) and local features (SIFT) have been introduced; however, the performance of these features can deteriorate for images captured in unconstrained and less-cooperative conditions. A particular set of Higher Order Spectral (HOS) features have been proved to be invariant to translation, scale, rotation, brightness level shift and contrast change. These properties are desirable in the periocular recognition problem to deal with the non-ideal imaging conditions. This paper investigates the HOS features in different configurations for the periocular recognition problem under non-ideal conditions. Especially, we introduce a new sampling approach for the periocular region based on an elliptical coordinate. This non-linear sampling approach is then combined with the robustness of the HOS features for encoding the periocular region. In addition, we also propose a new technique for combining left and right periocular. The proposed feature-level fusion approach bases on state-of-the-art bilinear pooling technique to allow efficient interaction between the features of both perioculars. We show the validity of the proposed approach in encoding discriminant features, outperforming or comparing favorably with the state-of-the-art features on the two popular datasets: FRGC and JAFFE

An Efficient Boosted Classifier Tree-Based Feature Point Tracking System for Facial Expression Analysis

The study of facial movement and expression has been a prominent area of research since the early work of Charles Darwin. The Facial Action Coding System (FACS), developed by Paul Ekman, introduced the first universal method of coding and measuring facial movement. Human-Computer Interaction seeks to make human interaction with computer systems more effective, easier, safer, and more seamless. Facial expression recognition can be broken down into three distinctive subsections: Facial Feature Localization, Facial Action Recognition, and Facial Expression Classification. The first and most important stage in any facial expression analysis system is the localization of key facial features. Localization must be accurate and efficient to ensure reliable tracking and leave time for computation and comparisons to learned facial models while maintaining real-time performance. Two possible methods for localizing facial features are discussed in this dissertation. The Active Appearance Model is a statistical model describing an object\u27s parameters through the use of both shape and texture models, resulting in appearance. Statistical model-based training for object recognition takes multiple instances of the object class of interest, or positive samples, and multiple negative samples, i.e., images that do not contain objects of interest. Viola and Jones present a highly robust real-time face detection system, and a statistically boosted attentional detection cascade composed of many weak feature detectors. A basic algorithm for the elimination of unnecessary sub-frames while using Viola-Jones face detection is presented to further reduce image search time. A real-time emotion detection system is presented which is capable of identifying seven affective states (agreeing, concentrating, disagreeing, interested, thinking, unsure, and angry) from a near-infrared video stream. The Active Appearance Model is used to place 23 landmark points around key areas of the eyes, brows, and mouth. A prioritized binary decision tree then detects, based on the actions of these key points, if one of the seven emotional states occurs as frames pass. The completed system runs accurately and achieves a real-time frame rate of approximately 36 frames per second. A novel facial feature localization technique utilizing a nested cascade classifier tree is proposed. A coarse-to-fine search is performed in which the regions of interest are defined by the response of Haar-like features comprising the cascade classifiers. The individual responses of the Haar-like features are also used to activate finer-level searches. A specially cropped training set derived from the Cohn-Kanade AU-Coded database is also developed and tested. Extensions of this research include further testing to verify the novel facial feature localization technique presented for a full 26-point face model, and implementation of a real-time intensity sensitive automated Facial Action Coding System

Automatic inference of latent emotion from spontaneous facial micro-expressions

Emotional states exert a profound influence on individuals' overall well-being, impacting them both physically and psychologically. Accurate recognition and comprehension of human emotions represent a crucial area of scientific exploration. Facial expressions, vocal cues, body language, and physiological responses provide valuable insights into an individual's emotional state, with facial expressions being universally recognised as dependable indicators of emotions. This thesis centres around three vital research aspects concerning the automated inference of latent emotions from spontaneous facial micro-expressions, seeking to enhance and refine our understanding of this complex domain. Firstly, the research aims to detect and analyse activated Action Units (AUs) during the occurrence of micro-expressions. AUs correspond to facial muscle movements. Although previous studies have established links between AUs and conventional facial expressions, no such connections have been explored for micro-expressions. Therefore, this thesis develops computer vision techniques to automatically detect activated AUs in micro-expressions, bridging a gap in existing studies. Secondly, the study explores the evolution of micro-expression recognition techniques, ranging from early handcrafted feature-based approaches to modern deep-learning methods. These approaches have significantly contributed to the field of automatic emotion recognition. However, existing methods primarily focus on capturing local spatial relationships, neglecting global relationships between different facial regions. To address this limitation, a novel third-generation architecture is proposed. This architecture can concurrently capture both short and long-range spatiotemporal relationships in micro-expression data, aiming to enhance the accuracy of automatic emotion recognition and improve our understanding of micro-expressions. Lastly, the thesis investigates the integration of multimodal signals to enhance emotion recognition accuracy. Depth information complements conventional RGB data by providing enhanced spatial features for analysis, while the integration of physiological signals with facial micro-expressions improves emotion discrimination. By incorporating multimodal data, the objective is to enhance machines' understanding of latent emotions and improve latent emotion recognition accuracy in spontaneous micro-expression analysis

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs