ANALYSIS OF FACIAL MARKS TO DISTINGUISH BETWEEN IDENTICAL TWINS USING NOVEL METHOD

Reliable and accurate verification of people is extremely important in a number of business transactions as well as access to privileged information. The biometrics-based methods assume that the physical characteristics of an individual (as captured by a sensor) used for verification are sufficiently unique to distinguish one person from another. But the increase in twin births has created a requirement for biometric systems to accurately determine the identity of a person who has an identical twin. Identical twins have the closest genetics-based relationship and, therefore, the maximum similarity between fingerprints is expected to be found among identical twins. They can’t be discriminated based on DNA. As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. Identical twin face recognition is a difficult task due to the existence of a high degree of correlation in overall facial appearance. In this paper, we study the usability of facial marks as biometric signatures to distinguish between identical twins. We propose a multi scale automatic facial mark detector based on a gradient-based operator known as the fast radial symmetry transform. The transform detects bright or dark regions with high radial symmetry at different scales. Next, the detections are tracked across scales to determine the prominence of facial marks. Extensive experiments are performed both on manually annotated and on automatically detected facial marks to evaluate the usefulness of facial marks as biometric signatures. The results of our analysis signify the usefulness of the distribution of facial marks as a biometric signature

Differentiation of Identical Twins by Facial Morphological Comparison: An Exploratory Study and Implications for Forensic Science

— This study aimed to explore the ability of facial morphological comparison to differentiate monozygotic twins and identify which facial components were most useful for this purpose. The research was carried out on facial images of 09 pairs of twins (18 people), where 12 facial components were identified using the morphological comparison method. Each of these components were compared in each pair of twins, so we identified those components that were similar or different. Subsequently, the frequencies of similarities and differences for each facial component were calculated. Next, an analysis of variance was applied between the components identified as different and similar. The results suggested that such a method was useful for differentiating identical twins and that some facial components were more useful than others. In this sample, facial markings and the ear were the most discriminating components. These results would set the tone for future research in this area

Identification of Identical Twins using Face Recognition with Results

Face recognition is a process used to identify or verify the person based on digital image from unique face of humans. Face recognition is based on individual and unique person identification. This process fully based on comparing the image with other person image for identification. Face Recognition is typically used in security systems and can be compared with other biometrics such as fingerprint or iris recognition systems. Here, the major problem is to identify twins. To overcome this problem we can use different facial recognition algorithms. The facial recognition algorithms should be able to identify the similar-looking individuals or identical Twins with accurate classification. In the proposed system, image of a person is given as a input then different features of image were extracted by using the Gabor and LBP algorithms. Extracted Features of both the images are compared and then classified using multi-SVM classifier. Based on classification method, the persons were identified to be identical twins or they were identified to be same person or not twins. After Identification, Performance of the process is measured

Twin identification over viewpoint change: A deep convolutional neural network surpasses humans

Deep convolutional neural networks (DCNNs) have achieved human-level accuracy in face identification (Phillips et al., 2018), though it is unclear how accurately they discriminate highly-similar faces. Here, humans and a DCNN performed a challenging face-identity matching task that included identical twins. Participants (N=87) viewed pairs of face images of three types: same-identity, general imposter pairs (different identities from similar demographic groups), and twin imposter pairs (identical twin siblings). The task was to determine whether the pairs showed the same person or different people. Identity comparisons were tested in three viewpoint-disparity conditions: frontal to frontal, frontal to 45-degree profile, and frontal to 90-degree profile. Accuracy for discriminating matched-identity pairs from twin-imposters and general imposters was assessed in each viewpoint-disparity condition. Humans were more accurate for general-imposter pairs than twin-imposter pairs, and accuracy declined with increased viewpoint disparity between the images in a pair. A DCNN trained for face identification (Ranjan et al., 2018) was tested on the same image pairs presented to humans. Machine performance mirrored the pattern of human accuracy, but with performance at or above all humans in all but one condition. Human and machine similarity scores were compared across all image-pair types. This item-level analysis showed that human and machine similarity ratings correlated significantly in six of nine image-pair types [range r=0.38 to r=0.63], suggesting general accord between the perception of face similarity by humans and the DCNN. These findings also contribute to our understanding of DCNN performance for discriminating high-resemblance faces, demonstrate that the DCNN performs at a level at or above humans, and suggest a degree of parity between the features used by humans and the DCNN

Identical twins : distinguishing between two in a set

The purposes of this study were to determine (1) whether three-year- old identical twin girls dressed alike and photographed in identical positions could be differentiated by the children and teachers in the Nursery School at the University of North Carolina at Greensboro (UNC-G) School of Home Economics, and (2) whether there were specific physical characteristics which children and teachers used to differentiate between the twin girls in identical photographs. The subjects for the study were 16 three- and four-year-old boys and girls, four teachers, and 11 student teachers involved in the Nursery School in the UNC-G School of Home Economics. Threeyear- old identical twin girls were photographed wearing identical clothing and in 10 identical positions. These photographs, presented as colored slides, were paired and presented to each of the subjects in four paired trials. The subjects were asked to identify one of the twins and their responses were recorded as being correct or incorrect. Data were analyzed by means of a three-factor ANOVA, a two-factor ANOVA, a t test for difference between independent means, and the Scheffé test for least significance difference

Photoplethysmogram Based Biometric Identification for Twins Incorporating Gender Variability

This study focuses on a Photoplethysmogram (PPG) based biometric identification for twins incorporating gender variability. To the best of our knowledge, little has been said pertaining to this research which identifies twins using PPG signals. PPG device has been widely used due to its advantages such as non-invasive, low cost and small in size which makes it a convenient analytical tool. PPG signals has the capability to ensure the person to be present during the acquisition process which suggest that PPG can provide liveness detection suitable for a biometric system which is not available in other biometric modalities such as fingerprint. A total of four couple of twins which consists of four female and four male subjects in age range between twenty two to thirty years old were used to assess the feasibility of the proposed system. The acquired PPG signals were then processed to remove unwanted noise using low pass filter. After that, multiple cycles of PPG waveforms were extracted and later classified using Radial Basis Function (RBF) and Bayes Network (BN) to categorize the subjects using the discriminant features to calculate and analyze the performance of this system. The outcome also provides a complimentary mechanism to detect twins besides using the current existing methods

IDENTITY CRISIS: WHEN FACE RECOGNITION MEETS TWINS AND PRIVACY

Ph.DDOCTOR OF PHILOSOPH