Evaluation of accurate eye corner detection methods for gaze estimation

Accurate detection of iris center and eye corners appears to be a promising approach for low cost gaze estimation. In this paper we propose novel eye inner corner detection methods. Appearance and feature based segmentation approaches are suggested. All these methods are exhaustively tested on a realistic dataset containing images of subjects gazing at different points on a screen. We have demonstrated that a method based on a neural network presents the best performance even in light changing scenarios. In addition to this method, algorithms based on AAM and Harris corner detector present better accuracies than recent high performance face points tracking methods such as Intraface

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

Automatic human face detection in color images

Automatic human face detection in digital image has been an active area of research over the past decade. Among its numerous applications, face detection plays a key role in face recognition system for biometric personal identification, face tracking for intelligent human computer interface (HCI), and face segmentation for object-based video coding. Despite significant progress in the field in recent years, detecting human faces in unconstrained and complex images remains a challenging problem in computer vision. An automatic system that possesses a similar capability as the human vision system in detecting faces is still a far-reaching goal. This thesis focuses on the problem of detecting human laces in color images. Although many early face detection algorithms were designed to work on gray-scale Images, strong evidence exists to suggest face detection can be done more efficiently by taking into account color characteristics of the human face. In this thesis, we present a complete and systematic face detection algorithm that combines the strengths of both analytic and holistic approaches to face detection. The algorithm is developed to detect quasi-frontal faces in complex color Images. This face class, which represents typical detection scenarios in most practical applications of face detection, covers a wide range of face poses Including all in-plane rotations and some out-of-plane rotations. The algorithm is organized into a number of cascading stages including skin region segmentation, face candidate selection, and face verification. In each of these stages, various visual cues are utilized to narrow the search space for faces. In this thesis, we present a comprehensive analysis of skin detection using color pixel classification, and the effects of factors such as the color space, color classification algorithm on segmentation performance. We also propose a novel and efficient face candidate selection technique that is based on color-based eye region detection and a geometric face model. This candidate selection technique eliminates the computation-intensive step of window scanning often employed In holistic face detection, and simplifies the task of detecting rotated faces. Besides various heuristic techniques for face candidate verification, we developface/nonface classifiers based on the naive Bayesian model, and investigate three feature extraction schemes, namely intensity, projection on face subspace and edge-based. Techniques for improving face/nonface classification are also proposed, including bootstrapping, classifier combination and using contextual information. On a test set of face and nonface patterns, the combination of three Bayesian classifiers has a correct detection rate of 98.6% at a false positive rate of 10%. Extensive testing results have shown that the proposed face detector achieves good performance in terms of both detection rate and alignment between the detected faces and the true faces. On a test set of 200 images containing 231 faces taken from the ECU face detection database, the proposed face detector has a correct detection rate of 90.04% and makes 10 false detections. We have found that the proposed face detector is more robust In detecting in-plane rotated laces, compared to existing face detectors. +D2

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Face modeling for face recognition in the wild.

Face understanding is considered one of the most important topics in computer vision field since the face is a rich source of information in social interaction. Not only does the face provide information about the identity of people, but also of their membership in broad demographic categories (including sex, race, and age), and about their current emotional state. Facial landmarks extraction is the corner stone in the success of different facial analyses and understanding applications. In this dissertation, a novel facial modeling is designed for facial landmarks detection in unconstrained real life environment from different image modalities including infra-red and visible images. In the proposed facial landmarks detector, a part based model is incorporated with holistic face information. In the part based model, the face is modeled by the appearance of different face part(e.g., right eye, left eye, left eyebrow, nose, mouth) and their geometric relation. The appearance is described by a novel feature referred to as pixel difference feature. This representation is three times faster than the state-of-art in feature representation. On the other hand, to model the geometric relation between the face parts, the complex Bingham distribution is adapted from the statistical community into computer vision for modeling the geometric relationship between the facial elements. The global information is incorporated with the local part model using a regression model. The model results outperform the state-of-art in detecting facial landmarks. The proposed facial landmark detector is tested in two computer vision problems: boosting the performance of face detectors by rejecting pseudo faces and camera steering in multi-camera network. To highlight the applicability of the proposed model for different image modalities, it has been studied in two face understanding applications which are face recognition from visible images and physiological measurements for autistic individuals from thermal images. Recognizing identities from faces under different poses, expressions and lighting conditions from a complex background is an still unsolved problem even with accurate detection of landmark. Therefore, a learning similarity measure is proposed. The proposed measure responds only to the difference in identities and filter illuminations and pose variations. similarity measure makes use of statistical inference in the image plane. Additionally, the pose challenge is tackled by two new approaches: assigning different weights for different face part based on their visibility in image plane at different pose angles and synthesizing virtual facial images for each subject at different poses from single frontal image. The proposed framework is demonstrated to be competitive with top performing state-of-art methods which is evaluated on standard benchmarks in face recognition in the wild. The other framework for the face understanding application, which is a physiological measures for autistic individual from infra-red images. In this framework, accurate detecting and tracking Superficial Temporal Arteria (STA) while the subject is moving, playing, and interacting in social communication is a must. It is very challenging to track and detect STA since the appearance of the STA region changes over time and it is not discriminative enough from other areas in face region. A novel concept in detection, called supporter collaboration, is introduced. In support collaboration, the STA is detected and tracked with the help of face landmarks and geometric constraint. This research advanced the field of the emotion recognition

3D Facial landmark detection under large yaw and expression variations

A 3D landmark detection method for 3D facial scans is presented and thoroughly evaluated. The main contribution of the presented method is the automatic and pose-invariant detection of landmarks on 3D facial scans under large yaw variations (that often result in missing facial data), and its robustness against large facial expressions. Three-dimensional information is exploited by using 3D local shape descriptors to extract candidate landmark points. The shape descriptors include the shape index, a continuous map of principal curvature values of a 3D object’s surface, and spin images, local descriptors of the object’s 3D point distribution. The candidate landmarks are identified and labeled by matching them with a Facial Landmark Model (FLM) of facial anatomical landmarks. The presented method is extensively evaluated against a variety of 3D facial databases and achieves state-of-the-art accuracy (4.5-6.3 mm mean landmark localization error), considerably outperforming previous methods, even when tested with the most challenging data

Visual attention and perception in scene understanding for social robotics

Ph.DDOCTOR OF PHILOSOPH

Doctor of Philosophy

dissertationThe embedded system space is characterized by a rapid evolution in the complexity and functionality of applications. In addition, the short time-to-market nature of the business motivates the use of programmable devices capable of meeting the conflicting constraints of low-energy, high-performance, and short design times. The keys to achieving these conflicting constraints are specialization and maximally extracting available application parallelism. General purpose processors are flexible but are either too power hungry or lack the necessary performance. Application-specific integrated circuits (ASICS) efficiently meet the performance and power needs but are inflexible. Programmable domain-specific architectures (DSAs) are an attractive middle ground, but their design requires significant time, resources, and expertise in a variety of specialties, which range from application algorithms to architecture and ultimately, circuit design. This dissertation presents CoGenE, a design framework that automates the design of energy-performance-optimal DSAs for embedded systems. For a given application domain and a user-chosen initial architectural specification, CoGenE consists of a a Compiler to generate execution binary, a simulator Generator to collect performance/energy statistics, and an Explorer that modifies the current architecture to improve energy-performance-area characteristics. The above process repeats automatically until the user-specified constraints are achieved. This removes or alleviates the time needed to understand the application, manually design the DSA, and generate object code for the DSA. Thus, CoGenE is a new design methodology that represents a significant improvement in performance, energy dissipation, design time, and resources. This dissertation employs the face recognition domain to showcase a flexible architectural design methodology that creates "ASIC-like" DSAs. The DSAs are instruction set architecture (ISA)-independent and achieve good energy-performance characteristics by coscheduling the often conflicting constraints of data access, data movement, and computation through a flexible interconnect. This represents a significant increase in programming complexity and code generation time. To address this problem, the CoGenE compiler employs integer linear programming (ILP)-based 'interconnect-aware' scheduling techniques for automatic code generation. The CoGenE explorer employs an iterative technique to search the complete design space and select a set of energy-performance-optimal candidates. When compared to manual designs, results demonstrate that CoGenE produces superior designs for three application domains: face recognition, speech recognition and wireless telephony. While CoGenE is well suited to applications that exhibit a streaming behavior, multithreaded applications like ray tracing present a different but important challenge. To demonstrate its generality, CoGenE is evaluated in designing a novel multicore N-wide SIMD architecture, known as StreamRay, for the ray tracing domain. CoGenE is used to synthesize the SIMD execution cores, the compiler that generates the application binary, and the interconnection subsystem. Further, separating address and data computations in space reduces data movement and contention for resources, thereby significantly improving performance compared to existing ray tracing approaches