A unified framework for thermal face recognition

The reduction of the cost of infrared (IR) cameras in recent years has made IR imaging a highly viable modality for face recognition in practice. A particularly attractive advantage of IR-based over conventional, visible spectrumbased face recognition stems from its invariance to visible illumination. In this paper we argue that the main limitation of previous work on face recognition using IR lies in its ad hoc approach to treating different nuisance factors which affect appearance, prohibiting a unified approach that is capable of handling concurrent changes in multiple (or indeed all) major extrinsic sources of variability, which is needed in practice. We describe the first approach that attempts to achieve this – the framework we propose achieves outstanding recognition performance in the presence of variable (i) pose, (ii) facial expression, (iii) physiological state, (iv) partial occlusion due to eye-wear, and (v) quasi-occlusion due to facial hair growth.

Mixed-norm sparse representation for multi view face recognition

Face recognition with multiple views is a challenging research problem. Most of the existing works have focused on extracting shared information among multiple views to improve recognition. However, when the pose variation is too large or missing, 'shared information' may not be properly extracted, leading to poor recognition results. In this paper, we propose a novel method for face recognition with multiple view images to overcome the large pose variation and missing pose issue. By introducing a novel mixed norm, the proposed method automatically selects candidates from the gallery to best represent a group of highly correlated face images in a query set to improve classification accuracy. This mixed norm combines the advantages of both sparse representation based classification (SRC) and joint sparse representation based classification (JSRC). A trade off between the ℓ1-norm from SRC and ℓ2,1-norm from JSRC is introduced to achieve this goal. Due to this property, the proposed method decreases the influence when a face image is unseen and has large pose variation in the recognition process. And when some face images with a certain degree of unseen pose variation appear, this mixed norm will find an optimal representation for these query images based on the shared information induced from multiple views. Moreover, we also address an open problem in robust sparse representation and classification which is using ℓ1-norm on the loss function to achieve a robust solution. To solve this formulation, we derive a simple, yet provably convergent algorithm based on the powerful alternative directions method of multipliers (ADMM) framework. We provide extensive comparisons which demonstrate that our method outperforms other state-of-the-arts algorithms on CMU-PIE, Yale B and Multi-PIE databases for multi-view face recognition.

A hybrid FMM-CART model for human activity recognition

In this paper, the application of a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) to human activity recognition is presented. The hybrid FMM-CART model capitalizes the merits of both FMM and CART in data classification and rule extraction. To evaluate the effectiveness of FMM-CART, two data sets related to human activity recognition problems are conducted. The results obtained are higher than those reported in the literature. More importantly, practical rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM- CART. This outcome positively indicates the potential of FMM- CART in undertaking human activity recognition tasks.

Multi-label approach for human-face classification

Single-label classification models have been widely used for human-face classification. In this paper, we present a multi-label classification approach for human-face classification. Multi-label classification is more appropriate in the real world because a human-face can be associated with multiple labels. Demographic information can be derived and utilized along with facial expression in the field of face classification to assist with multi label classification. Gabor filters; Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) methods, are used to extract and project representative demographic information from facial images. For evaluation, five classification algorithms were used. We evaluate the proposed approach by performing experiments on Yale face images database. Results show the effectiveness of multi-label classification algorithms.

Distance-driven fusion of gait and face for human identification in video

Gait and face are two important biometrics for human identification. Complementary properties of these two biometrics suggest fusion of them. The relationship between gait and face in the fusion is affected by the subject-to-camera distance. On the one hand, gait is a suitable biometric trait for human recognition at a distance. On the other hand, face recognition is more reliable when the subject is close to the camera. This paper proposes an adaptive fusion method called distance-driven fusion to combine gait and face for human identification in video. Rather than predefined fixed fusion rules, distance-driven fusion dynamically adjusts its rule according to the subject-to-camera distance in real time. Experimental results show that distance-driven fusion performs better than not only single biometric, but also the conventional
static fusion rules including MEAN, PRODUCT, MIN, and MAX.

Dynamic biometrics fusion at feature level for video-based human recognition

This paper proposes a novel human recognition method in video, which combines human face and gait traits
using a dynamic multi-modal biometrics fusion scheme. The Fisherface approach is adopted to extract face
features, while for gait features, Locality Preserving Projection (LPP) is used to achieve low-dimensional
manifold embedding of the temporal silhouette data derived from image sequences. Face and gait features are
fused dynamically at feature level based on a distance-driven fusion method. Encouraging experimental results
are achieved on the video sequences containing 20 people, which show that dynamically fused features produce
a more discriminating power than any individual biometric as well as integrated features built on common static
fusion schemes.

Eliciting children's recall of events: how do computers compare with humans?

This study investigated the usefulness of an interactive computer program in eliciting children's reports about an event. Fifty-four 5–6- and fifty-nine  7–8-year old children participated in an event with their regular class teacher which involved several activities and a mildly negative secret. Four days and again 14 days later, the children were interviewed individually by computer (alone) or by a human interviewer. The computer program incorporated animation and audio whereby an animated figure asked the questions and the children were required to provide a verbal response. The accuracy and detail of the children’s reports was similar across the interview conditions. The children were more willing to review their answers with the computer than the adult interviewer. However, responses to the computer were less consistent across the interviews, and the children were less willing to disclose the secret in the second interview to the computer compared with the human interviewer. Overall, the computer revealed little benefit in eliciting children’s recall of the event over the standard face-to-face interview.

Computer aided analysis of dental radiographic images

This paper is a result of a fruitful cooperation between the computer science and the dental diagnosis experiences. The study presents a new approach of applying computer algorithms to radiographic images of dental implantation used for bone regeneration. We focus here only on the contribution of the computer assistance to the clinical research as the periodontal therapy is beyond the scope of this paper. The proposed system is based on a pattern recognition approach, directed to recognize density changes in the intra-bony affected areas of patients. It comprises different modules with new algorithms specially designed to treat the patients’ radiographic images more accurately. The system includes digitizing, detecting the complicated region of interest (ROI), defining reference area to correct any projection discrepancy of the follow up images, and finally to extract the distinguishing features of the ROI as a basis for determining the rate of new bone density accumulation. This study is applied to two typical dental cases for a patient who received two different operations. The results are very encouraging and more accurate than traditional techniques reported before.

On the incremental learning and recognition of the pattern of movement of multiple labelled objects in dynamic scenes

In this paper we discuss combining incremental learning and incremental recognition to classify patterns consisting of multiple objects, each represented by multiple spatio-temporal features. Importantly the technique allows for ambiguity in terms of the positions of the start and finish of the pattern. This involves a progressive classification which considers the data at each time instance in the query and thus provides a probable answer before all the query information becomes available. We present two methods that combine incremental learning and incremental recognition: a time instance method and an overall best match method.

Joint learning and dictionary construction for pattern recognition

We propose a joint representation and classification framework that achieves the dual goal of finding the most discriminative sparse overcomplete encoding and optimal classifier parameters. Formulating an optimization problem that combines the objective function of the classification with the representation error of both labeled and unlabeled data, constrained by sparsity, we propose an algorithm that alternates between solving for subsets of parameters, whilst preserving the sparsity. The method is then evaluated over two important classification problems in computer vision: object categorization of natural images using the Caltech 101 database and face recognition using the Extended Yale B face database. The results show that the proposed method is competitive against other recently proposed sparse overcomplete counterparts and considerably outperforms many recently proposed face recognition techniques when the number training samples is small.

Online context recognition in multisensor systems using Dynamic Time Warping

In this paper, we present our system for online context recognition of multimodal sequences acquired from multiple sensors. The system uses Dynamic Time Warping (DTW) to recognize multimodal sequences of different lengths, embedded in continuous data streams. We evaluate the performance of our system on two real world datasets: 1) accelerometer data acquired from performing two hand gestures and 2) NOKIA's benchmark dataset for context recognition. The results from both datasets demonstrate that the system can perform online context recognition efficiently and achieve high recognition accuracy.