Thermal Face Recognition Based on Physiological Information

In this paper, an automatic age simulation method used for robust face recognition is proposed. We first use a shape and texture vectors to represent a facial image by projecting it in the eigenspace of shape or texture. Then we use age function combined with aging way classification to estimate age. And we use estimated age, typical vector creating function and the feature vector of the original test image to generate the synthesized feature vectors at target age. At last we reconstruct the shape and texture in eigenspaces and combine them to synthesize facial image at target age. Experiments show that the proposed method can effectively "change" the age of face images, and help to get face recognition result robust to age variation

One unresolved question about face perception is: what is the role of three-dimensional information in face recognition? In this study, recognition performance was compared across changes in viewpoint in different depth conditions: a 2D condition without stereo information and a 3D condition where stereo information was present (by viewing the same face images as anaglyphs through 3D glasses). Subjects' eye movements were recorded during both 3D and 2D sessions. The findings revealed that participants were more accurate in the 3D condition. Moreover, individual differences in interpupillary distance predicted recognition performance in the 3D but not in the 2D condition. A "region of interest" analysis of gaze data showed that rich volumetric properties provided by certain facial features (e.g., the nose and the cheeks) were attended more in the 3D condition compared to the 2D condition. Taken together, these findings support the conclusion that face recognition across viewpoint transformation is facilitated by the addition of stereoscopic depth cues.

Face recognition is used as one of the most successful biometric methods due to the availability of advanced resources such as faster processors and higher memory and providing intelligent methods based on the power of these resources. Nevertheless, there are still many challenges in this area. The face plays an important role in the transmission of emotions and carries the characteristics hidden in it, the identity of individuals. Face recognition has been added to some control devices, security, welfare, criminal identification, and many other areas, which is the main motivation for research in this field. In this paper, the DCSFR method is presented to pay attention to the main features of the face such as eyes, lips, mouth, and nose, which is the main novelty of this work, to get higher accuracy or speed than the previously existing methods. In this approach, instead of using general information in face recognition, facial components such as eyes, nose, mouth are separated into another image, and face classification operations (deep learning by convolution neural network) are performed on separated components. The results show that the computational cost with the proposed method is reduced by about 70%. Also, it can be achieved that CNN does not perform as well as the complete picture of the disassembled components.

The automatic recognition of human faces presents a significant challenge to the pattern recognition research community. Typically, human faces are very similar in structure with minor differences from person to person. They are actually within one class of "human face". Furthermore, lighting conditions change, while facial expressions and pose variations further complicate the face recognition task as one of the difficult problems in pattern analysis. This paper proposes a novel concept: namely, that faces can be recognized using a line edge map (LEM). The LEM, a compact face feature, is generated for face coding and recognition. A thorough investigation of the proposed concept is conducted which covers all aspects of human face recognition, i.e. face recognition under (1) controlled/ideal conditions and size variations, (2) varying lighting conditions, (3) varying facial expressions, and (4) varying pose. The system performance is also compared with the eigenface method, one of the best face recognition techniques, and with reported experimental results of other methods. A face pre-filtering technique is proposed to speed up the search process. It is a very encouraging to find that the proposed face recognition technique has performed better than the eigenface method in most of the comparison experiments. This research demonstrates that the LEM, together with the proposed generic line-segment Hausdorff distance measure, provides a new method for face coding and recognition.

These Human facial expressions convey a lot of information visually rather than articulately. Facial expression recognition plays a crucial role in the area of human-machine interaction. Automatic facial expression recognition system has many applications including, but not limited to, human behaviour understanding, detection of mental disorders, and synthetic human expressions. Recognition of facial expression by computer with high recognition rate is still a challenging task. Two popular methods utilized mostly in the literature for the automatic FER systems are based on geometry and appearance. Facial Expression Recognition usually performed in four-stages consisting of pre-processing, face detection, feature extraction, and expression classification. The human face is an important part of an individual’s body and plays an important role in knowing the individual’s mood. The face is where a human expresses all his basic emotions. In the existing system, they examine the mental state manually by assessing them but have many disadvantages like we cannot predict any accurate solutions based on the assessment score because we might be not sure what kind of emotions the human user would be all time. To overcome this problem, a novel system is proposed to suggest an effective solution for predicting the mental state dynamically, we propose a hybrid architecture invoking facial based emotion sequence, PEN test and IQ test. By consistent monitoring of a human’s emotion and subjecting to PEN and IQ tests, the human’s mental state is routed. Combination of the above three techniques provides promising results for mental state and self-control

Aiming at the problem of low recognition rate caused by local deformations such as pose, angle, expression change and occlusion, especially small local deformations in face recognition. A face recognition algorithm based on Gabor wavelet transform and central symmetric local direction pattern (CSLDP) is proposed. Gabor is used to extract multi-dimensional features, and CSLDP is used to reduce the computational complexity. Then histogram feature information is divided into blocks and counted in series as feature vectors of face images. Finally, sparse representation classifier (SRC) is used for classification and recognition. The experimental results on ORL, YALE and AR face databases show that the proposed method can adapt to the changes of fine expressions and light, and has strong robustness.

Machine learning and pattern recognition recently become a hot topic in computing world. This is due to the fast-growing of resources as well as techniques that make it easier to solve machine learning and pattern recognition problems. Problems that require machine learning solutions may be very simple for humans but actually can be very complex for machines to solve them. Face recognition is amongst those problems. Almost all human can easily recognize others without require specific knowledge to do it, different from machines which require its. This paper discussed face recognition task using machine learning strategies which involved Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) to identify person. KPCA extracted features from 2D image input and produced the important features of an image input. The extracted face features are recognized by SVM by classifying human face according to their stored identity in a database. SVM, which was basically a binary classifier worked by using one-against-one strategy to compare the face feature vector of a single test image to the stored face image in a face image database. Experiment results on grayscale images with size 92x112 pixels gave 96.25% of accuracy rate. Hence, KPCA and SVM for face recognition is a robust machine learning method.

The current dominant approaches to face recognition rely on facial characteristics that are on or over the skin. Some of these characteristics have low permanency can be altered, and their phenomenology varies significantly with environmental factors (e.g., lighting). Many methodologies have been developed to address these problems to various degrees. However, the current framework of face recognition research has a potential weakness due to its very nature. We present a novel framework for face recognition based on physiological information. The motivation behind this effort is to capitalize on the permanency of innate characteristics that are under the skin. To establish feasibility, we propose a specific methodology to capture facial physiological patterns using the bioheat information contained in thermal imagery. First, the algorithm delineates the human face from the background using the Bayesian framework. Then, it localizes the superficial blood vessel network using image morphology. The extracted vascular network produces contour shapes that are characteristic to each individual. The branching points of the skeletonized vascular network are referred to as Thermal Minutia Points (TMPs) and constitute the feature database. To render the method robust to facial pose variations, we collect for each subject to be stored in the database five different pose images (center, midleft profile, left profile, midright profile, and right profile). During the classification stage, the algorithm first estimates the pose of the test image. Then, it matches the local and global TMP structures extracted from the test image with those of the corresponding pose images in the database. We have conducted experiments on a multipose database of thermal facial images collected in our laboratory, as well as on the time-gap database of the University of Notre Dame. The good experimental results show that the proposed methodology has merit, especially with respect to the problem of low permanence over time. More importantly, the results demonstrate the feasibility of the physiological framework in face recognition and open the way for further methodological and experimental research in the area.

A multiscale approach to exploiting existing image descriptors (LBP and HOG) is proposed recently in order to enhance face recognition performance (Ubiquitous computing and ambient intelligence. Personalisation and user adapted services. Springer, 532–539, 2014) and (A multiscale method for HOG-based face and palmprint recognition. Technical report, Ulster University, 2015), where multiple single-sourced, spatially-varied feature vectors at different scales are calculated from images and then fused through an image distance function. This multiscale approach has led to significant improvements in face recognition over the single scale approach. In this paper we present an analysis of this multiscale approach from feature engineering perspective and evaluation result for the image distance function on palmprint recognition, thus providing an insight into and also extending the applicability of this approach. We also present a new method of utilising these spatially-varied feature vectors from an image—joining these feature vectors head to tail to form a larger feature vector which is used as a multiplex representation of the image. Such an image representation can then be used by any vector-based feature extraction and classification algorithms. This representation scheme is evaluated experimentally in face recognition, and the results show this scheme is competitive to the distance-based method having the additional advantage of being usable in a wider range of machine learning algorithms. The main contributions of this paper are (1) an insight into this multiscale approach to utilising existing image descriptors such as LBP and HOG; (2) a new method of using these multiple feature vectors; and (3) extension of the multiscale approach to palmprint recognition.

The equivalence of two-dimensional PCA to line-based PCA

In the current scenario face identification and recognition is an important technique in surveillance. The face is a necessary biometric in humans. Therefore face detection plays a major job in computer vision applications. Several face recognition and emotions classification approaches have been presented throughout the last few decades of research to improve the rate of face recognition for thermal pictures. However, in real-time, lighting conditions might change due to several factors, such as the different times of capture, weather, etc. Due to variations in lighting intensity, the performance of the facial expression recognition system is not good. This paper proposed a model for human thermal face detection and expression classification. Four main steps were involved in this research. Initially, the Difference of the Gaussian (DOG) filter is utilized to crop the input thermal images and then normalize the images using the median filter in pre-processing step. Then, Efficient Net is used for extracting features such as shape, location, and occurrences from thermal face images. After that, detect human faces utilized by the YOLOv4 technique to better emotions classification. Finally, classify the emotions on faces by using the DenseNet technique into seven emotions such as happy, sad, disgust, surprise, anger, fear, and neutral. The proposed method outperforms state-of-art techniques for face recognition on thermal pictures, and classifies the expressions, according to experimentations on the RGB-D-T database. The accuracy, precision, recall, and f1-score metrics will be utilized with the database to assess the efficacy of the proposed methodology. The proposed models achieve a high classification accuracy of 95.97% on the RGB-D-T database. Furthermore, the outcomes show good precision for various face recognition tasks.

In e-learning environment, the main function of the tutor is to provide assistance to one or a group of learners. We distinguish between two types of tutors: The reactive tutor who responds simply to the explicit requests of the learner and the proactive tutor who takes the initiative to provide support to the learner and offer him advice at the appropriate time. Our goal is to create an environment where the proactive role of tutor is to assist and monitor a single learner during the learning process. In our case tutor should have his own problem-solving analysis, his own ability to model the learner and his own expertise in generating explanations. In this paper, an automatic tutor based on face recognition and emotion recognition system is proposed for a distant learning platform, the first step for face recognition system is face detection for this we are using the viola jones object detect algorithm that can provide the detection in real time. Features extraction and tracking is the base of emotion detection for that the KLT algorithm will be applied after face detection. These algorithms will be performed in Matlab as programming tool focused in image processing and facial recognition. This proposed method is used to create an automatic tutor that helps assisting the learner during an educational process in e-learning. The name of this Facial and Audio Recognition Automatic Tutoring Model is FARAT.

Content-Based Image Retrieval (CBIR) has become one of the trending areas of research in computer vision. In traditional CBIR, the features in a spatial domain, such as color, texture, shape, and point features, are extracted. It is often considered that apart from the spatial features, the features extracted from the frequency domain of the images can give further information on the features of an image. In this paper, we are proposing two novel methods for the purpose of feature extraction from the 2-dimensional Discrete Cosine Transform (DCT) of an image: DCT_256_Zigzag and DCT_256_2 × 2. These methods take into consideration the lower frequencies in order to determine the features in the frequency domain. The advantage of using the zigzag scanning is to have the maximum low frequency values having Higher Energies comparatively. These two features are combined with two of the existing spatial domain features: Local Binary Patterns (LBP) and Interchannel voting features to generate a global feature vector for an image. For a query image, its feature vector is compared with feature vectors of every other image in the database using d1-distance, and the images with least distance are considered most similar images to the query image. To evaluate the efficiency of these two methods, five standard performance measures such as Average Precision Rate (APR), Average Recall Rate (ARR), F-Measure, Average Normalized Modified Retrieval Rank (ANMRR), and Total Minimum Retrieval Epoch (TMRE) are used. Six benchmark image datasets Corel-1000, Corel-5000, Corel-10000, VisTex, STex, and Color-Brodatz are used to corroborate the performance of these methods.KeywordsCBIRDCTFrequency domainInterchannel votingLBPTMRE

This paper proposes a content-based authentication scheme for tampering detection and localization of binary images. The watermark is generated by the feature vector of the original binary image, and embedded back into the image by a structural method. The feature vector we used is Zernike moments. It can tell the degree of the tamper in the binary image. A distance between two feature vectors is defined. We authenticate image by the distance between the extracted watermark and the feature vector of the test image. Once the tampering is detected, we can locate the tampered areas by comparing different components of the distance. The experimental results show that the algorithm can locate the tamper effectively.

Face recognition is a process by which the identity of a person is determined from the face images stored in a face database. Face recognition is one of the most successful applications of image analysis. In the present scenario, face recognition plays a major role in commercial and law enforcement applications, such as surveillance system, passport, security, personal information accesses, human machine interaction, etc. At present, very reliable methods of biometric personal identification exist. In face recognition, a feature vector usually represents the salient characteristics that best describe a face image. However, these characteristics vary quite substantially while looking into a face image from different directions. This chapter addresses this issue by means of image fusion and presents a comprehensive study of different image fusion techniques for face recognition. Image fusion is done between the original captured image and its true/partial diagonal images.