Realtime human face tracking and recognition system on uncontrolled environment

Recently, one of the most important biometrics is that automatically recognized human faces are based on dynamic facial images with different rotations and backgrounds. This paper presents a real-time system for human face tracking and recognition with various expressions of the face, poses, and rotations in an uncontrolled environment (dynamic background). Many steps are achieved in this paper to enhance, detect, and recognize the faces from the image frame taken by web-camera. The system has three steps: the first is to detect the face, Viola-Jones algorithm is used to achieve this purpose for frontal and profile face detection. In the second step, the color space algorithm is used to track the detected face from the previous step. The third step, principal component analysis (eigenfaces) algorithm is used to recognize faces. The result shows the effectiveness and robustness depending on the training and testing results. The real-time system result is compared with the results of the previous papers and gives a success, effectiveness, and robustness recognition rate of 91.12% with a low execution time. However, the execution time is not fixed due depending on the frame background and specification of the web camera and computer

Pose Invariant Face Recognition and Tracking for Human Identification

Real-time tracking and recognition of people in complex environments has been a widely researched area in computer vision as it has a huge potential in efficient security automation and surveillance. We propose a real time system for detection and recognition of individuals in a scene by detecting, recognizing and tracking faces. The system integrates the multi-view face detection algorithm, the multi-pose face recognition algorithm and the extended multi-pose Kalman face tracker. The multi-view face detection algorithm contains the frontal face and profile face detectors which extract the Haar-like features and detect faces at any pose by a cascade of boosted classifiers. The pose of the face is inherently determined from the face detection algorithm and is used in the multi-pose face recognition module where depending on the pose, the detected face is compared with a particular set of trained faces having the same pose range. The pose range of the trained faces is divided into bins onto which the faces are sorted and each bin is trained separately to have its own Eigenspace. The human faces are recognized by projecting them onto a suitable Eigenspace corresponding to the determined pose using Weighted Modular Principal Component Analysis (WMPCA) technique and then, are tracked using the proposed multiple face tracker. This tracker is implemented by extracting suitable face features which are represented by a variant of WMPCA and then tracking these features across the scene using the Kalman filter. This low-level system is created using the same face database of twenty unrelated people trained using WMPCA and classification is performed using a feature correlation metric. This system has the advantage of recognizing and tracking an individual in a cluttered environment with varying pose variations.https://ecommons.udayton.edu/stander_posters/1240/thumbnail.jp

Ancient Roman coin recognition in the wild using deep learning based recognition of artistically depicted face profiles

As a particularly interesting application in the realm of cultural heritage on the one hand, and a technically challenging problem, computer vision based analysis of Roman Imperial coins has been attracting an increasing amount of research. In this paper we make several important contributions. Firstly, we address a key limitation of existing work which is largely characterized by the application of generic object recognition techniques and the lack of use of domain knowledge. In contrast, our work approaches coin recognition in much the same way as a human expert would: by identifying the emperor universally shown on the obverse.To this end we develop a deep convolutional network, carefully crafted for what is effectively a specific instance of profile face recognition. No less importantly, we also address a major methodological flaw of previous research which is, as we explain in detail, insufficiently systematic and rigorous,and mired with confounding factors. Lastly, we introduce three carefully collected and annotated data sets, and using these demonstrate the effectiveness of the proposed approach which is shown to exceed the performance of the state of the art by approximately an order of magnitude.Postprin

A New Algorithm to Classify Face Emotions through Eye and Lip Features by Using Particle Swarm Optimization

Abstract. Facial expressions give important clues about emotions. Computer systems based on affective interaction could play an important role in the next generation of biometric surveillance systems. Face emotion recognition is one of the main applications of computer vision that is widely attended in recent years and can be used in areas of security, entertainment and human machine interface (HMI). The research of emotion recognition consists of facial expressions, vocal, gesture and physiology signal recognition and etc. In this paper a new algorithm based on a set of images to face emotion recognition has been proposed. This process involves three stages: pre-processing, feature extraction and classification. Firstly a series of preprocessing tasks such as adjusting contrast, filtering, skin color segmentation and edge detection are done. One of the important tasks at this stage after pre-processing is to extract features. To extract features with high speed projection profile is used. Second particle swarm optimization (PSO) is used to optimize eye and lip ellipse characteristics. Finally in the third stage, with using the features obtained of the optimal ellipse eye and lip, a person emotion according to experimental results and emotions represented by Ekman (sadness, angry, joy, fear, disgust and surprise without consider natural emotion) is classified. The obtained results show that the success rate and running speed in face emotion recognition in comparison with previous methods has better performance

Side-View Face Recognition

Side-view face recognition is a challenging problem with many applications. Especially in real-life scenarios where the environment is uncontrolled, coping with pose variations up to side-view positions is an important task for face recognition. In this paper we discuss the use of side view face recognition techniques to be used in house safety applications. Our aim is to recognize people as they pass through a door, and estimate their location in the house. Here, we compare available databases appropriate for this task, and review current methods for profile face recognition

Facial Asymmetry Analysis Based on 3-D Dynamic Scans

Facial dysfunction is a fundamental symptom which often relates to many neurological illnesses, such as stroke, Bell’s palsy, Parkinson’s disease, etc. The current methods for detecting and assessing facial dysfunctions mainly rely on the trained practitioners which have significant limitations as they are often subjective. This paper presents a computer-based methodology of facial asymmetry analysis which aims for automatically detecting facial dysfunctions. The method is based on dynamic 3-D scans of human faces. The preliminary evaluation results testing on facial sequences from Hi4D-ADSIP database suggest that the proposed method is able to assist in the quantification and diagnosis of facial dysfunctions for neurological patients

Reference face graph for face recognition

Face recognition has been studied extensively; however, real-world face recognition still remains a challenging task. The demand for unconstrained practical face recognition is rising with the explosion of online multimedia such as social networks, and video surveillance footage where face analysis is of significant importance. In this paper, we approach face recognition in the context of graph theory. We recognize an unknown face using an external reference face graph (RFG). An RFG is generated and recognition of a given face is achieved by comparing it to the faces in the constructed RFG. Centrality measures are utilized to identify distinctive faces in the reference face graph. The proposed RFG-based face recognition algorithm is robust to the changes in pose and it is also alignment free. The RFG recognition is used in conjunction with DCT locality sensitive hashing for efficient retrieval to ensure scalability. Experiments are conducted on several publicly available databases and the results show that the proposed approach outperforms the state-of-the-art methods without any preprocessing necessities such as face alignment. Due to the richness in the reference set construction, the proposed method can also handle illumination and expression variation