Multiscale Retinex Application to Analyze Face Recognition

The main challenge that facial recognition introduces is the difficulty of uneven lighting or dark tendencies. The image is poorly lit, which makes it difficult for the system to perform facial recognition. This study aims to normalize the lighting in the image using the Multiscale Retinex method. This method is applied to a face recognition system based on Principal Component Analysis to determine whether this method effectively improves images with uneven lighting. The results showed that the Multiscale Retinex approach to face recognition's correctness was better, from 40% to 76%. Multiscale Retinex has the advantage of dark facial image types because it produces a brighter image output

Impact evaluation of skin color, gender, and hair on the performance of eigenface, ICA, and, CNN methods

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceAlthough face recognition has made remarkable progress in the past decades, it is still a challenging area. In addition to traditional flaws (such as illumination, pose, occlusion in part of face image), the low performance of the system with dark skin images and female faces raises questions that challenge transparency and accountability of the system. Recent work has suggested that available datasets are causing this issue, but little work has been done with other face recognition methods. Also, little work has been done on facial features such as hair as a key face feature in the face recognition system. To address the gaps this thesis examines the performance of three face recognition methods (eigenface, Independent Component Analysis (ICA) and Convolution Neuron Network (CNN)) with respect to skin color changes in two different face mode “only face” and “face with hair”. The following work is reported in this study, 1st rebuild approximate PPB dataset based on work done by “Joy Adowaa Buolamwini” in her thesis entitled “Gender shades”. 2nd new classifier tools developed, and the approximate PPB dataset classified based on new methods in 12 classes. 3rd the three methods assessed with approximate PPB dataset in two face mode. The evaluation of the three methods revealed an interesting result. In this work, the eigenface method performs better than ICA and CNN. Moreover, the result shows a strong positive correlation between the numbers of train sets and results that it can prove the previous finding about lack of image with dark skin. More interestingly, despite the claims, the models showed a proactive behavior in female’s face identification. Despite the female group shape 21% of the population in the top two skin type groups, the result shows 44% of the top 3 recall for female groups. Also, it confirms that adding hair to images in average boosts the results by up to 9%. The work concludes with a discussion of the results and recommends the impact of classes on each other for future stud

Color inversion and detail effects on face recognition

Two separate studies were completed to demonstrate the importance of color location and focus on face recognition. The first study manipulated Gaussian Blur (GB) and inversion (IN). GB is the process of taking an image out of focus, the higher the cycle the more out of focus the image will appear. IN is the process of changing the dark color with light color and the light color with dark color, like a colored photographic negative. In the study, twenty celebrity faces (10 female and 10 male) were exposed to six different manipulations: three levels of GB and two levels of IN (present and absent). Each of the 41 participants was exposed to all 120 images. Results showed that as the GB increased, there was a decrease in performance. When IN was present, there was also a decrease in performance. However, when GB and IN were used in combination, performance did not decrease further. The second study manipulated higher levels of GB and Glowing Edge (GE). GE is the process of highlighting the contours of the face in different colors. One hundred twenty participants were randomly exposed to one of the six conditions following one practice list. The results were measured using a between subjects design which showed an interaction between GB and GE, indicating both were a contributor to face recognition. It was demonstrated that facial recognition is contingent upon proper color location

Methodology for Evaluating Statistical Equivalence in Face Recognition Using Live Subjects with Dissimilar Skin Tones

The general purpose of this study is to propose a methodology that can be employed in the application of facial recognition systems (FRS) to determine if a statistically significant difference exists in a facial recognition system’s ability to match two dissimilar skin tone populations to their enrolled images. A particular objective is to test the face recognition system’s ability to recognize dark or light skin tone subjects. In addition to the direct comparison of results from two different populations, this study uses a Box Behnken Design to examine four factors commonly effecting facial recognition systems. Four factors were tested, the horizontal angle of the camera viewing the subject, both horizontally to the left and right; the vertical angle, both above and below the subject’s line of sight, ;the distance the subjects are from the camera, and the intensity of the illumination on the subject. Experimentation was approached from the assumption that subjects are cooperative, following guidelines for proper enrollment and submission for matching. The experimentation of the four factors was conducted using two sets of three subjects. One set was dark skin tone males, and the second set was light skin tone males. The results of the study showed a significance statistical difference at p = 0.05 level between the two skin tones, with greater difficulty identifying the light skin tone test subjects than those with dark skin tone

Preprocessing Technique for Face Recognition Applications under Varying illumination Conditions

In the last years, face recognition has become a popular area of research in computer vision, it is typically used in network security systems and access control systems but it is also useful in other multimedia information processing areas. Performance of the face verification system depends on many conditions. One of the most problematic is varying illumination condition. In this paper, we discuss the preprocessing method to solve one of the common problems in face images, due to a real capture system i.e. lighting variations. The different stages include gamma correction, Difference of Gaussian (DOG) filtering and contrast equalization. Gamma correction enhances the local dynamic range of the image in dark or shadowed regions while compressing it in bright regions and is determined by the value of 3B3;. DOG filtering is a grey scale image enhancement algorithm that eliminates the shadowing effects. Contrast equalization rescales the image intensities to standardize a robust measure of overall intensity variations. The technique has been applied to Yale-B data sets, Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 and a real time created data set

Eye contrast polarity is critical for face recognition by infants

Just as faces share the same basic arrangement of features, with two eyes above a nose above a mouth, human eyes all share the same basic contrast polarity relations, with a sclera lighter than an iris and a pupil, and this is unique among primates. The current study examined whether this bright-dark relationship of sclera to iris plays a critical role in face recognition from early in development. Specifically, we tested face discrimination in 7- and 8-month-old infants while independently manipulating the contrast polarity of the eye region and of the rest of the face. This gave four face contrast polarity conditions: fully positive condition, fully negative condition, positive face with negated eyes ( negative eyes ) condition, and negated face with positive eyes ( positive eyes ) condition. In a familiarization and novelty preference procedure, we found that 7- and 8-month-olds could discriminate between faces only when the contrast polarity of the eyes was preserved (positive) and that this did not depend on the contrast polarity of the rest of the face. This demonstrates the critical role of eye contrast polarity for face recognition in 7- and 8-month-olds and is consistent with previous findings for adults

Methodology for Evaluating Statistical Equivalence in Face Recognition Using Live Subjects with Dissimilar Skin Tones

The general purpose of this study is to propose a methodology that can be employed in the application of facial recognition systems (FRS) to determine if a statistically significant difference exists in a facial recognition system’s ability to match two dissimilar skin tone populations to their enrolled images. A particular objective is to test the face recognition system’s ability to recognize dark or light skin tone subjects. In addition to the direct comparison of results from two different populations, this study uses a Box Behnken Design to examine four factors commonly effecting facial recognition systems. Four factors were tested, the horizontal angle of the camera viewing the subject, both horizontally to the left and right; the vertical angle, both above and below the subject’s line of sight, ;the distance the subjects are from the camera, and the intensity of the illumination on the subject. Experimentation was approached from the assumption that subjects are cooperative, following guidelines for proper enrollment and submission for matching. The experimentation of the four factors was conducted using two sets of three subjects. One set was dark skin tone males, and the second set was light skin tone males. The results of the study showed a significance statistical difference at p = 0.05 level between the two skin tones, with greater difficulty identifying the light skin tone test subjects than those with dark skin tone