Acquisition scenario analysis for face recognition at a distance

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-17289-2_44Proceedings of 6th International Symposium, ISVC 2010, Las Vegas, NV, (USA)An experimental analysis of three acquisition scenarios for face recognition at a distance is reported, namely: close, medium, and far distance between camera and query face, the three of them considering templates enrolled in controlled conditions. These three representative scenarios are studied using data from the NIST Multiple Biometric Grand Challenge, as the first step in order to understand the main variability factors that affect face recognition at a distance based on realistic yet workable and widely available data. The scenario analysis is conducted quantitatively in two ways. First, we analyze the information content in segmented faces in the different scenarios. Second, we analyze the performance across scenarios of three matchers, one commercial, and two other standard approaches using popular features (PCA and DCT) and matchers (SVM and GMM). The results show to what extent the acquisition setup impacts on the verification performance of face recognition at a distance.This work has been partially supported by projects Bio-Challenge (TEC2009-11186), Contexts (S2009/TIC-1485), TeraSense (CSD2008-00068) and "Cátedra UAM-Telefónica"

On Using High-Definition Body Worn Cameras for Face Recognition from a Distance

Recognition of human faces from a distance is highly desirable for law-enforcement. This paper evaluates the use of low-cost, high-definition (HD) body worn video cameras for face recognition from a distance. A comparison of HD vs. Standard-definition (SD) video for face recognition from a distance is presented. HD and SD videos of 20 subjects were acquired in different conditions and at varying distances. The evaluation uses three benchmark algorithms: Eigenfaces, Fisherfaces and Wavelet Transforms. The study indicates when gallery and probe images consist of faces captured from a distance, HD video result in better recognition accuracy, compared to SD video. This scenario resembles real-life conditions of video surveillance and law-enforcement activities. However, at a close range, face data obtained from SD video result in similar, if not better recognition accuracy than using HD face data of the same range

Shafiyyatul Amaliyyah School Student Face Absence Using Principal Component Analysis and K – Nearest Neighbor

Pattern recognition is one of the sciences used to classify things based on quantitative measurements of the main features or properties of an object. Pattern recognition has been widely used in various fields of research. One of the pattern recognition that is often discussed is facial recognition. The face is one of the human biometrics that is often used as the main information of a person. Face recognition is a field of research with many applications in applications such as attendance, population data collection, security systems, and others. The research utilizes feature extraction of PCA (Principal Component Analysis), and K-NN (K – Nearest Neighbor) with variations of the distance formula by applying facial recognition attendance at the Safiatul Amaliyah School. This research is expected to get accurate results in detecting, recognizing, and comparing a person's face with a small error rate. The distance formula with accuracy level is presented with the equation Cityblock < Euclidian < Minkowski < Chebychev. The effect of applying the variation of the distance formula on the performance of the facial attendance recognition model is not too big, but it is better

Low-resolution face alignment and recognition using mixed-resolution classifiers

A very common case for law enforcement is recognition of suspects from a long distance or in a crowd. This is an important application for low-resolution face recognition (in the authors' case, face region below 40 × 40 pixels in size). Normally, high-resolution images of the suspects are used as references, which will lead to a resolution mismatch of the target and reference images since the target images are usually taken at a long distance and are of low resolution. Most existing methods that are designed to match high-resolution images cannot handle low-resolution probes well. In this study, they propose a novel method especially designed to compare low-resolution images with high-resolution ones, which is based on the log-likelihood ratio (LLR). In addition, they demonstrate the difference in recognition performance between real low-resolution images and images down-sampled from high-resolution ones. Misalignment is one of the most important issues in low-resolution face recognition. Two approaches - matching-score-based registration and extended training of images with various alignments - are introduced to handle the alignment problem. Their experiments on real low-resolution face databases show that their methods outperform the state-of-the-art

The effect of time on gait recognition performance

Many studies have shown that it is possible to recognize people by the way they walk. However, there are a number of covariate factors that affect recognition performance. The time between capturing the gallery and the probe has been reported to affect recognition the most. To date, no study has shown the isolated effect of time, irrespective of other covariates. Here we present the first principled study that examines the effect of elapsed time on gait recognition. Using empirical evidence we show for the first time that elapsed time does not affect recognition significantly in the short to medium term. By controlling the clothing worn by the subjects and the environment, a Correct Classification Rate (CCR) of 95% has been achieved over 9 months, on a dataset of 2280 gait samples. Our results show that gait can be used as a reliable biometric over time and at a distance. We have created a new multimodal temporal database to enable the research community to investigate various gait and face covariates. We have also investigated the effect of different type of clothes, variations in speed and footwear on the recognition performance. We have demonstrated that clothing drastically affects performance regardless of elapsed time and significantly more than any of the other covariates that we have considered here. The research then suggests a move towards developing appearance invariant recognition algorithms. Thi

BLACKFACE SURVEILLANCE CAMERA DATABASE FOR EVALUATING FACE RECOGNITION IN LOW QUALITY SCENARIOS

Many face recognition algorithms perform poorly in real life surveillance scenarios because they were tested with datasets that are already biased with high quality images and certain ethnic or racial types. In this paper a black face surveillance camera (BFSC) database was described, which was collected from four low quality cameras and a professional camera. There were fifty (50) random volunteers and 2,850 images were collected for the frontal mugshot, surveillance (visible light), surveillance (IR night vision), and pose variations datasets, respectively. Images were taken at distance 3.4, 2.4, and 1.4 metres from the camera, while the pose variation images were taken at nine distinct pose angles with an increment of 22.5 degrees to the left and right of the subject. Three Face Recognition Algorithms (FRA), a commercially available Luxand SDK, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) were evaluated for performance comparison in low quality scenarios. Results obtained show that camera quality (resolution), face-to-camera distance, average recognition time, lighting conditions and pose variations all affect the performance of FRAs. Luxand SDK, PCA and LDA returned an overall accuracy of 97.5%, 93.8% and 92.9% after categorizing the BFSC images into excellent, good and acceptable quality scales.