A study of eigenvector based face verification in static images

As one of the most successful application of image analysis and understanding, face recognition has recently received significant attention, especially during the past few years. There are at least two reasons for this trend the first is the wide range of commercial and law enforcement applications and the second is the availability of feasible technologies after 30 years of research. The problem of machine recognition of human faces continues to attract researchers from disciplines such as image processing, pattern recognition, neural networks, computer vision, computer graphics, and psychology. The strong need for user-friendly systems that can secure our assets and protect our privacy without losing our identity in a sea of numbers is obvious. Although very reliable methods of biometric personal identification exist, for example, fingerprint analysis and retinal or iris scans, these methods depend on the cooperation of the participants, whereas a personal identification system based on analysis of frontal or profile images of the face is often effective without the participant’s cooperation or knowledge. The three categories of face recognition are face detection, face identification and face verification. Face Detection means extract the face from total image of the person. Face identification means the input to the system is an unknown face, and the system reports back the determined identity from a database of known individuals. Face verification means the system needs to confirm or reject the claimed identity of the input. My thesis was face verification in static images. Here a static image means the images which are not in motion. The eigenvectors based face verification algorithm gave the results on face verification in static images based upon the eigenvectors and neural network backpropagation algorithm. Eigen vectors are used for give the geometrical information about the faces. First we take 10 images for each person in same angle with different expressions and apply principle component analysis. Here we consider image dimension as 48 x48 then we get 48 eigenvalues. Out of 48 eigenvalues we consider only 10 highest eigenvaues corresponding eigenvectors. These eigenvectors are given as input to the neural network for training. Here we used backpropagation algorithm for training the neural network. After completion of training we give an image which is in different angle for testing purpose. Here we check the verification rate (the rate at which legitimate users is granted access) and false acceptance rate (the rate at which imposters are granted access). Here neural network take more time for training purpose. The proposed algorithm gives the results on face verification in static images based upon the eigenvectors and neural network modified backpropagation algorithm. In modified backpropagation algorithm momentum term is added for decrease the training time. Here for using the modified backpropagation algorithm verification rate also slightly increased and false acceptance rate also slightly decreased

Geometrical-based lip-reading using template probabilistic multi-dimension dynamic time warping

By identifying lip movements and characterizing their associations with speech sounds, the performance of speech recognition systems can be improved, particularly when operating in noisy environments. In this paper, we present a geometrical-based automatic lip reading system that extracts the lip region from images using conventional techniques, but the contour itself is extracted using a novel application of a combination of border following and convex hull approaches. Classification is carried out using an enhanced dynamic time warping technique that has the ability to operate in multiple dimensions and a template probability technique that is able to compensate for differences in the way words are uttered in the training set. The performance of the new system has been assessed in recognition of the English digits 0 to 9 as available in the CUAVE database. The experimental results obtained from the new approach compared favorably with those of existing lip reading approaches, achieving a word recognition accuracy of up to 71% with the visual information being obtained from estimates of lip height, width and their ratio

Facial feature processing using artificial neural networks

Describing a human face is a natural ability used in eveyday life. To the police, a witness description of a suspect is key evidence in the identification of the suspect. However, the process of examining "mug shots" to find a match to the description is tedious and often unfruitful. If a description could be stored with each photograph and used as a searchable index, this would provide a much more effective means of using "mug shots" for identification purposes. A set of descriptive measures have been defined by Shepherd [73] which seek to describe faces in a manner that may be used for just this purpose. This work investigates methods of automatically determining these descriptive measures from digitised images. Analysis is performed on the images to establish the potential for distinguishing between different categories in these descriptions. This reveals that while some of the classifications are relatively linear, others are very non-linear. Artificial neural networks (ANNs), being often used as non-linear classifiers, are considered as a means of automatically performing the classification of the images. As a comparison, simple linear classifiers are also applied to the same problems

Facial feature representation and recognition

Facial expression provides an important behavioral measure for studies of emotion, cognitive processes, and social interaction. Facial expression representation and recognition have become a promising research area during recent years. Its applications include human-computer interfaces, human emotion analysis, and medical care and cure. In this dissertation, the fundamental techniques will be first reviewed, and the developments of the novel algorithms and theorems will be presented later. The objective of the proposed algorithm is to provide a reliable, fast, and integrated procedure to recognize either seven prototypical, emotion-specified expressions (e.g., happy, neutral, angry, disgust, fear, sad, and surprise in JAFFE database) or the action units in CohnKanade AU-coded facial expression image database. A new application area developed by the Infant COPE project is the recognition of neonatal facial expressions of pain (e.g., air puff, cry, friction, pain, and rest in Infant COPE database). It has been reported in medical literature that health care professionals have difficulty in distinguishing newborn\u27s facial expressions of pain from facial reactions of other stimuli. Since pain is a major indicator of medical problems and the quality of patient care depends on the quality of pain management, it is vital that the methods to be developed should accurately distinguish an infant\u27s signal of pain from a host of minor distress signal. The evaluation protocol used in the Infant COPE project considers two conditions: person-dependent and person-independent. The person-dependent means that some data of a subject are used for training and other data of the subject for testing. The person-independent means that the data of all subjects except one are used for training and this left-out one subject is used for testing. In this dissertation, both evaluation protocols are experimented. The Infant COPE research of neonatal pain classification is a first attempt at applying the state-of-the-art face recognition technologies to actual medical problems. The objective of Infant COPE project is to bypass these observational problems by developing a machine classification system to diagnose neonatal facial expressions of pain. Since assessment of pain by machine is based on pixel states, a machine classification system of pain will remain objective and will exploit the full spectrum of information available in a neonate\u27s facial expressions. Furthermore, it will be capable of monitoring neonate\u27s facial expressions when he/she is left unattended. Experimental results using the Infant COPE database and evaluation protocols indicate that the application of face classification techniques in pain assessment and management is a promising area of investigation. One of the challenging problems for building an automatic facial expression recognition system is how to automatically locate the principal facial parts since most existing algorithms capture the necessary face parts by cropping images manually. In this dissertation, two systems are developed to detect facial features, especially for eyes. The purpose is to develop a fast and reliable system to detect facial features automatically and correctly. By combining the proposed facial feature detection, the facial expression and neonatal pain recognition systems can be robust and efficient

Out-of-plane action unit recognition using recurrent neural networks

A dissertation submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of requirements for the degree of Master of Science. Johannesburg, 2015.The face is a fundamental tool to assist in interpersonal communication and interaction between people. Humans use facial expressions to consciously or subconsciously express their emotional states, such as anger or surprise. As humans, we are able to easily identify changes in facial expressions even in complicated scenarios, but the task of facial expression recognition and analysis is complex and challenging to a computer. The automatic analysis of facial expressions by computers has applications in several scientific subjects such as psychology, neurology, pain assessment, lie detection, intelligent environments, psychiatry, and emotion and paralinguistic communication. We look at methods of facial expression recognition, and in particular, the recognition of Facial Action Coding System’s (FACS) Action Units (AUs). Movements of individual muscles on the face are encoded by FACS from slightly different, instant changes in facial appearance. Contractions of specific facial muscles are related to a set of units called AUs. We make use of Speeded Up Robust Features (SURF) to extract keypoints from the face and use the SURF descriptors to create feature vectors. SURF provides smaller sized feature vectors than other commonly used feature extraction techniques. SURF is comparable to or outperforms other methods with respect to distinctiveness, robustness, and repeatability. It is also much faster than other feature detectors and descriptors. The SURF descriptor is scale and rotation invariant and is unaffected by small viewpoint changes or illumination changes. We use the SURF feature vectors to train a recurrent neural network (RNN) to recognize AUs from the Cohn-Kanade database. An RNN is able to handle temporal data received from image sequences in which an AU or combination of AUs are shown to develop from a neutral face. We are recognizing AUs as they provide a more fine-grained means of measurement that is independent of age, ethnicity, gender and different expression appearance. In addition to recognizing FACS AUs from the Cohn-Kanade database, we use our trained RNNs to recognize the development of pain in human subjects. We make use of the UNBC-McMaster pain database which contains image sequences of people experiencing pain. In some cases, the pain results in their face moving out-of-plane or some degree of in-plane movement. The temporal processing ability of RNNs can assist in classifying AUs where the face is occluded and not facing frontally for some part of the sequence. Results are promising when tested on the Cohn-Kanade database. We see higher overall recognition rates for upper face AUs than lower face AUs. Since keypoints are globally extracted from the face in our system, local feature extraction could provide improved recognition results in future work. We also see satisfactory recognition results when tested on samples with out-of-plane head movement, showing the temporal processing ability of RNNs

A generic face processing framework: technologies, analyses and applications.

Jang Kim-fung.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 108-124).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Introduction about Face Processing Framework --- p.4Chapter 1.2.1 --- Basic architecture --- p.4Chapter 1.2.2 --- Face detection --- p.5Chapter 1.2.3 --- Face tracking --- p.6Chapter 1.2.4 --- Face recognition --- p.6Chapter 1.3 --- The scope and contributions of the thesis --- p.7Chapter 1.4 --- The outline of the thesis --- p.8Chapter 2 --- Facial Feature Representation --- p.10Chapter 2.1 --- Facial feature analysis --- p.10Chapter 2.1.1 --- Pixel information --- p.11Chapter 2.1.2 --- Geometry information --- p.13Chapter 2.2 --- Extracting and coding of facial feature --- p.14Chapter 2.2.1 --- Face recognition --- p.15Chapter 2.2.2 --- Facial expression classification --- p.38Chapter 2.2.3 --- Other related work --- p.44Chapter 2.3 --- Discussion about facial feature --- p.48Chapter 2.3.1 --- Performance evaluation for face recognition --- p.49Chapter 2.3.2 --- Evolution of the face recognition --- p.52Chapter 2.3.3 --- Evaluation of two state-of-the-art face recog- nition methods --- p.53Chapter 2.4 --- Problem for current situation --- p.58Chapter 3 --- Face Detection Algorithms and Committee Ma- chine --- p.61Chapter 3.1 --- Introduction about face detection --- p.62Chapter 3.2 --- Face Detection Committee Machine --- p.64Chapter 3.2.1 --- Review of three approaches for committee machine --- p.65Chapter 3.2.2 --- The approach of FDCM --- p.68Chapter 3.3 --- Evaluation --- p.70Chapter 4 --- Facial Feature Localization --- p.73Chapter 4.1 --- Algorithm for gray-scale image: template match- ing and separability filter --- p.73Chapter 4.1.1 --- Position of face and eye region --- p.74Chapter 4.1.2 --- Position of irises --- p.75Chapter 4.1.3 --- Position of lip --- p.79Chapter 4.2 --- Algorithm for color image: eyemap and separa- bility filter --- p.81Chapter 4.2.1 --- Position of eye candidates --- p.81Chapter 4.2.2 --- Position of mouth candidates --- p.83Chapter 4.2.3 --- Selection of face candidates by cost function --- p.84Chapter 4.3 --- Evaluation --- p.85Chapter 4.3.1 --- Algorithm for gray-scale image --- p.86Chapter 4.3.2 --- Algorithm for color image --- p.88Chapter 5 --- Face Processing System --- p.92Chapter 5.1 --- System architecture and limitations --- p.92Chapter 5.2 --- Pre-processing module --- p.93Chapter 5.2.1 --- Ellipse color model --- p.94Chapter 5.3 --- Face detection module --- p.96Chapter 5.3.1 --- Choosing the classifier --- p.96Chapter 5.3.2 --- Verifying the candidate region --- p.97Chapter 5.4 --- Face tracking module --- p.99Chapter 5.4.1 --- Condensation algorithm --- p.99Chapter 5.4.2 --- Tracking the region using Hue color model --- p.101Chapter 5.5 --- Face recognition module --- p.102Chapter 5.5.1 --- Normalization --- p.102Chapter 5.5.2 --- Recognition --- p.103Chapter 5.6 --- Applications --- p.104Chapter 6 --- Conclusion --- p.106Bibliography --- p.10

Innovative local texture descriptors with application to eye detection

Local Binary Patterns (LBP), which is one of the well-known texture descriptors, has broad applications in pattern recognition and computer vision. The attractive properties of LBP are its tolerance to illumination variations and its computational simplicity. However, LBP only compares a pixel with those in its own neighborhood and encodes little information about the relationship of the local texture with the features. This dissertation introduces a new Feature Local Binary Patterns (FLBP) texture descriptor that can compare a pixel with those in its own neighborhood as well as in other neighborhoods and encodes the information of both local texture and features. The features encoded in FLBP are broadly defined, such as edges, Gabor wavelet features, and color features. Specifically, a binary image is first derived by extracting feature pixels from a given image, and then a distance vector field is obtained by computing the distance vector between each pixel and its nearest feature pixel defined in the binary image. Based on the distance vector field and the FLBP parameters, the FLBP representation of the given image is derived. The feasibility of the proposed FLBP is demonstrated on eye detection using the BioID and the FERET databases. Experimental results show that the FLBP method significantly improves upon the LBP method in terms of both the eye detection rate and the eye center localization accuracy. As LBP is sensitive to noise especially in near-uniform image regions, Local Ternary Patterns (LTP) was proposed to address this problem by extending LBP to three-valued codes. However, further research reveals that both LTP and LBP achieve similar results for face and facial expression recognition, while LTP has a higher computational cost than LBP. To improve upon LTP, this dissertation introduces another new local texture descriptor: Local Quaternary Patterns (LQP) and its extension, Feature Local Quaternary Patterns (FLQP). LQP encodes four relationships of local texture, and therefore, it includes more information of local texture than the LBP and the LTP. FLQP, which encodes both local and feature information, is expected to perform even better than LQP for texture description and pattern analysis. The LQP and FLQP are applied to eye detection on the BioID database. Experimental results show that both FLQP and LQP achieve better eye detection performance than FLTP, LTP, FLBP and LBP. The FLQP method achieves the highest eye detection rate

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