Cooperation of different neuronal systems during hand sign recognition.

Hand signs with symbolic meaning can often be utilized more successfully than words to communicate an intention; however, the underlying brain mechanisms are undefined. The present study using magnetoencephalography (MEG) demonstrates that the primary visual, mirror neuron, social recognition and object recognition systems are involved in hand sign recognition. MEG detected well-orchestrated multiple brain regional electrical activity among these neuronal systems. During the assessment of the meaning of hand signs, the inferior parietal, superior temporal sulcus (STS) and inferior occipitotemporal regions were simultaneously activated. These three regions showed similar time courses in their electrical activity, suggesting that they work together during hand sign recognition by integrating information in the ventral and dorsal pathways through the STS. The results also demonstrated marked right hemispheric predominance, suggesting that hand expression is processed in a manner similar to that in which social signs, such as facial expressions, are processed

Infrared face recognition: a comprehensive review of methodologies and databases

Automatic face recognition is an area with immense practical potential which includes a wide range of commercial and law enforcement applications. Hence it is unsurprising that it continues to be one of the most active research areas of computer vision. Even after over three decades of intense research, the state-of-the-art in face recognition continues to improve, benefitting from advances in a range of different research fields such as image processing, pattern recognition, computer graphics, and physiology. Systems based on visible spectrum images, the most researched face recognition modality, have reached a significant level of maturity with some practical success. However, they continue to face challenges in the presence of illumination, pose and expression changes, as well as facial disguises, all of which can significantly decrease recognition accuracy. Amongst various approaches which have been proposed in an attempt to overcome these limitations, the use of infrared (IR) imaging has emerged as a particularly promising research direction. This paper presents a comprehensive and timely review of the literature on this subject. Our key contributions are: (i) a summary of the inherent properties of infrared imaging which makes this modality promising in the context of face recognition, (ii) a systematic review of the most influential approaches, with a focus on emerging common trends as well as key differences between alternative methodologies, (iii) a description of the main databases of infrared facial images available to the researcher, and lastly (iv) a discussion of the most promising avenues for future research.Comment: Pattern Recognition, 2014. arXiv admin note: substantial text overlap with arXiv:1306.160

Automated design of robust discriminant analysis classifier for foot pressure lesions using kinematic data

In the recent years, the use of motion tracking systems for acquisition of functional biomechanical gait data, has received increasing interest due to the richness and accuracy of the measured kinematic information. However, costs frequently restrict the number of subjects employed, and this makes the dimensionality of the collected data far higher than the available samples. This paper applies discriminant analysis algorithms to the classification of patients with different types of foot lesions, in order to establish an association between foot motion and lesion formation. With primary attention to small sample size situations, we compare different types of Bayesian classifiers and evaluate their performance with various dimensionality reduction techniques for feature extraction, as well as search methods for selection of raw kinematic variables. Finally, we propose a novel integrated method which fine-tunes the classifier parameters and selects the most relevant kinematic variables simultaneously. Performance comparisons are using robust resampling techniques such as Bootstrap$632+$and k-fold cross-validation. Results from experimentations with lesion subjects suffering from pathological plantar hyperkeratosis, show that the proposed method can lead to$sim 96$correct classification rates with less than 10% of the original features

Machine Analysis of Facial Expressions

No abstract

Pain Level Detection From Facial Image Captured by Smartphone

Accurate symptom of cancer patient in regular basis is highly concern to the medical service provider for clinical decision making such as adjustment of medication. Since patients have limitations to provide self-reported symptoms, we have investigated how mobile phone application can play the vital role to help the patients in this case. We have used facial images captured by smart phone to detect pain level accurately. In this pain detection process, existing algorithms and infrastructure are used for cancer patients to make cost low and user-friendly. The pain management solution is the first mobile-based study as far as we found today. The proposed algorithm has been used to classify faces, which is represented as a weighted combination of Eigenfaces. Here, angular distance, and support vector machines (SVMs) are used for the classification system. In this study, longitudinal data was collected for six months in Bangladesh. Again, cross-sectional pain images were collected from three different countries: Bangladesh, Nepal and the United States. In this study, we found that personalized model for pain assessment performs better for automatic pain assessment. We also got that the training set should contain varying levels of pain in each group: low, medium and high

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

Geometric Expression Invariant 3D Face Recognition using Statistical Discriminant Models

Currently there is no complete face recognition system that is invariant to all facial expressions. Although humans find it easy to identify and recognise faces regardless of changes in illumination, pose and expression, producing a computer system with a similar capability has proved to be particularly di cult. Three dimensional face models are geometric in nature and therefore have the advantage of being invariant to head pose and lighting. However they are still susceptible to facial expressions. This can be seen in the decrease in the recognition results using principal component analysis when expressions are added to a data set. In order to achieve expression-invariant face recognition systems, we have employed a tensor algebra framework to represent 3D face data with facial expressions in a parsimonious space. Face variation factors are organised in particular subject and facial expression modes. We manipulate this using single value decomposition on sub-tensors representing one variation mode. This framework possesses the ability to deal with the shortcomings of PCA in less constrained environments and still preserves the integrity of the 3D data. The results show improved recognition rates for faces and facial expressions, even recognising high intensity expressions that are not in the training datasets. We have determined, experimentally, a set of anatomical landmarks that best describe facial expression e ectively. We found that the best placement of landmarks to distinguish di erent facial expressions are in areas around the prominent features, such as the cheeks and eyebrows. Recognition results using landmark-based face recognition could be improved with better placement. We looked into the possibility of achieving expression-invariant face recognition by reconstructing and manipulating realistic facial expressions. We proposed a tensor-based statistical discriminant analysis method to reconstruct facial expressions and in particular to neutralise facial expressions. The results of the synthesised facial expressions are visually more realistic than facial expressions generated using conventional active shape modelling (ASM). We then used reconstructed neutral faces in the sub-tensor framework for recognition purposes. The recognition results showed slight improvement. Besides biometric recognition, this novel tensor-based synthesis approach could be used in computer games and real-time animation applications