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.

The Effect of Training Data Selection on Face Recognition in Surveillance Application

Face recognition is an important biometric method because of its potential applications in many fields, such as access control and surveillance. In surveillance applications, the distance between the subject and the camera is changing. Thus, in this paper, the effect of the distance between the subject and the camera, distance class, the effect of the number of images per class, and also the effect of session used to acquire the images have been investigated. Three sessions are used to acquire the images in the database. The images in each session were equally divided into three distance classes: CLOSE, MEDIUM, and FAR, according to the distance of the subject from the camera. It was found that using images from the MEDIUM class for training gives better performance than using either the FAR or the CLOSE class. In addition, it was also found that using one image from each class for training gives the same recognition performance as using three images from the MEDIUM class for training. It was also found that as the number of images per class increases, the recognition performance also increases. Lastly, it was found that by using one image per class from all the available database sessions gives the best recognition performance.</p

Within-person variability in men's facial width-to-height ratio

Background. In recent years, researchers have investigated the relationship between facial width-to-height ratio (FWHR) and a variety of threat and dominance behaviours. The majority of methods involved measuring FWHR from 2D photographs of faces. However, individuals can vary dramatically in their appearance across images, which poses an obvious problem for reliable FWHR measurement. Methods. I compared the effect sizes due to the differences between images taken with unconstrained camera parameters (Studies 1 and 2) or varied facial expressions (Study 3) to the effect size due to identity, i.e., the differences between people. In Study 1, images of Hollywood actors were collected from film screenshots, providing the least amount of experimental control. In Study 2, controlled photographs, which only varied in focal length and distance to camera, were analysed. In Study 3, images of different facial expressions, taken in controlled conditions, were measured. Results. Analyses revealed that simply varying the focal length and distance between the camera and face had a relatively small effect on FWHR, and therefore may prove less of a problem if uncontrolled in study designs. In contrast, when all camera parameters (including the camera itself) are allowed to vary, the effect size due to identity was greater than the effect of image selection, but the ranking of the identities was significantly altered by the particular image used. Finally, I found significant changes to FWHR when people posed with four of seven emotional expressions in comparison with neutral, and the effect size due to expression was larger than differences due to identity. Discussion. The results of these three studies demonstrate that even when head pose is limited to forward facing, changes to the camera parameters and a person's facial expression have sizable effects on FWHR measurement. Therefore, analysing images that fail to constrain some of these variables can lead to noisy and unreliable results, but also relationships caused by previously unconsidered confounds

DETECTION OF SOMEONE'S CHARACTER BASED ON FACE SHAPE USING THE CANNY METHOD

Character is a unique way of interacting by individuals in creating a relationship. When interacting with people, it requires us to be face to face. The face is a very important element in communicating because from the face we can see a person's expression and the person's facial pattern so that their character can be known. The face is considered a reflection of a person's character so that a science called physiognomy has emerged. Physiognomy science is usually only known by experts, to get an easier way, technology can help provide solutions. The solution is to use a camera by taking a picture of the face whose character you want to understand, then doing a digital image processing (PCD). In this PCD process, there are several processes for processing images in order to obtain information from the image. One way is to use canny edge detection. Canny edge detection is used to identify or recognize object boundary lines in the image after the canny edge detection process is completed. The next process is to recognize face patterns by adding the euclidean distance method so that the face shape pattern can be recognized. The results of facial recognition test using the Canny and Euclidean distance method from 40 facial images, the percentage of success is 80%. &nbsp

Smart Home Security Menggunakan Face Recognition Dengan Metode Eigenface Berbasis Raspberry Pi

One of the biometric technologies that have been implemented in many security systems besides retinal recognition, fingerprint recognition and iris is facial recognition. On the hardware side itself, face recognition (Face Recognition) uses a camera to capture a person's face then compared to the previous face that has been stored in a particular database. There are several methods of facial recognition, namely neural networks, artificial neural networks, adaptive neuro fuzzy, and eigenface. Specifically in this study the method to be explained is the eigenface method. Specifically in this study the method that will be explained is the eigenface method, and uses a web cam to capture images in real time. The advantage of this method is that the computation is very fast and simple compared to the use of methods that require a lot of learning, such as artificial network requirements. Broadly speaking, the process of this application is the camera to capture faces, then an RGB value is obtained. Using the initial processing, resize, RGB to Grayscale, and histogram equalization for light alignment. The eigenface method functions to calculate the eigenvalue, and the eigenvector that will be used as a feature in making recognition. From the experiments and tests carried out, the tool can recognize facial images with a success rate of up to 90% at a distance of 25 cm with an average success of 72.5%. This proves this tool is quite good in face recognition