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

Generation of High Performing Morph Datasets

Facial recognition systems play a vital role in our everyday lives. We rely on this technology from menial tasks to issues as vital as national security. While strides have been made over the past ten years to improve facial recognition systems, morphed face images are a viable threat to the reliability of these systems. Morphed images are generated by combining the face images of two subjects. The resulting morphed face shares the likeness of the contributing subjects, confusing both humans and face verification algorithms. This vulnerability has grave consequences for facial recognition systems used on international borders or for law enforcement purposes. To detect these morph images, high-quality data must be generated to improve deep morph detectors. In this work, high-quality morph images are generated to fool these deep morph detection algorithms. This work creates some of the most challenging large-scale morphed datasets to date. This is done in three ways. First, rather than utilizing typical datasets used for face morphing found in literature, we generate morphed data from underrepresented groups of individuals to further increase the difficulty of morphs. Second, we generate morph subjects using a wavelet decomposition blending technique to generate morph images that may perform better than typical landmark morphs while creating morph images that may appear different to detectors than what is seen in literature. Third, we apply adversarial perturbation to the morph images to further increase their attack capability on morph detectors. Using these techniques, the generated morph datasets are highly successful at fooling facial recognition systems into erroneously classifying a morph as a bona fide subject

Correlations between holistic processing, Autism quotient, extraversion, and experience and the own-gender bias in face recognition.

The variability in the own-gender bias (OGB) in face-recognition is thought to be based on experience and the engagement of expert face processing mechanisms for own-gender faces. Experience is also associated with personality characteristics such as extraversion and Autism, yet the effects of these variables on the own-gender bias has not been explored. We ran a face recognition study exploring the relationships between own-gender experience, holistic processing (measured using the face-inversion effect, composite face effect, and the parts-and-wholes test), personality characteristics (extraversion and Autism Quotient) and the OGB. Findings did not support a mediational account where experience increases holistic processing and this increases the OGB. Rather, there was a direct relationship between extraversion and Autism Quotient and the OGB. We interpret this as personality characteristics having an effect on the motivation to process own-gender faces more deeply than opposite-gender faces

Multimodal biometrics scheme based on discretized eigen feature fusion for identical twins identification

The subject of twins multimodal biometrics identification (TMBI) has consistently been an interesting and also a valuable area of study. Considering high dependency and acceptance, TMBI greatly contributes to the domain of twins identification in biometrics traits. The variation of features resulting from the process of multimodal biometrics feature extraction determines the distinctive characteristics possessed by a twin. However, these features are deemed as inessential as they cause the increase in the search space size and also the difficulty in the generalization process. In this regard, the key challenge is to single out features that are deemed most salient with the ability to accurately recognize the twins using multimodal biometrics. In identification of twins, effective designs of methodology and fusion process are important in assuring its success. These processes could be used in the management and integration of vital information including highly selective biometrics characteristic possessed by any of the twins. In the multimodal biometrics twins identification domain, exemplification of the best features from multiple traits of twins and biometrics fusion process remain to be completely resolved. This research attempts to design a new scheme and more effective multimodal biometrics twins identification by introducing the Dis-Eigen feature-based fusion with the capacity in generating a uni-representation and distinctive features of numerous modalities of twins. First, Aspect United Moment Invariant (AUMI) was used as global feature in the extraction of features obtained from the twins handwritingfingerprint shape and style. Then, the feature-based fusion was examined in terms of its generalization. Next, to achieve better classification accuracy, the Dis-Eigen feature-based fusion algorithm was used. A total of eight distinctive classifiers were used in executing four different training and testing of environment settings. Accordingly, the most salient features of Dis-Eigen feature-based fusion were trained and tested to determine the accuracy of the classification, particularly in terms of performance. The results show that the identification of twins improved as the error of similarity for intra-class decreased while at the same time, the error of similarity for inter-class increased. Hence, with the application of diverse classifiers, the identification rate was improved reaching more than 93%. It can be concluded from the experimental outcomes that the proposed method using Receiver Operation Characteristics (ROC) considerably increases the twins handwriting-fingerprint identification process with 90.25% rate of identification when False Acceptance Rate (FAR) is at 0.01%. It is also indicated that 93.15% identification rate is achieved when FAR is at 0.5% and 98.69% when FAR is at 1.00%. The new proposed solution gives a promising alternative to twins identification application

Investigation of hierarchical deep neural network structure for facial expression recognition

Facial expression recognition (FER) is still a challenging concept, and machines struggle to comprehend effectively the dynamic shifts in facial expressions of human emotions. The existing systems, which have proven to be effective, consist of deeper network structures that need powerful and expensive hardware. The deeper the network is, the longer the training and the testing. Many systems use expensive GPUs to make the process faster. To remedy the above challenges while maintaining the main goal of improving the accuracy rate of the recognition, we create a generic hierarchical structure with variable settings. This generic structure has a hierarchy of three convolutional blocks, two dropout blocks and one fully connected block. From this generic structure we derived four different network structures to be investigated according to their performances. From each network structure case, we again derived six network structures in relation to the variable parameters. The variable parameters under analysis are the size of the filters of the convolutional maps and the max-pooling as well as the number of convolutional maps. In total, we have 24 network structures to investigate, and six network structures per case. After simulations, the results achieved after many repeated experiments showed in the group of case 1; case 1a emerged as the top performer of that group, and case 2a, case 3c and case 4c outperformed others in their respective groups. The comparison of the winners of the 4 groups indicates that case 2a is the optimal structure with optimal parameters; case 2a network structure outperformed other group winners. Considerations were done when choosing the best network structure, considerations were; minimum accuracy, average accuracy and maximum accuracy after 15 times of repeated training and analysis of results. All 24 proposed network structures were tested using two of the most used FER datasets, the CK+ and the JAFFE. After repeated simulations the results demonstrate that our inexpensive optimal network architecture achieved 98.11 % accuracy using the CK+ dataset. We also tested our optimal network architecture with the JAFFE dataset, the experimental results show 84.38 % by using just a standard CPU and easier procedures. We also compared the four group winners with other existing FER models performances recorded recently in two studies. These FER models used the same two datasets, the CK+ and the JAFFE. Three of our four group winners (case 1a, case 2a and case 4c) recorded only 1.22 % less than the accuracy of the top performer model when using the CK+ dataset, and two of our network structures, case 2a and case 3c came in third, beating other models when using the JAFFE dataset.Electrical and Mining Engineerin

Validation of the facial comparative morphological method, for identification of people through photographic registration, Lima-2021

La presente tesis, tiene como objetivo principal evaluar la validez del método morfológico comparativo para su aplicación en la identificación facial, lo cual se logrará mediante la medición de la exactitud, precisión, y tasas del falsos positivos y falsos negativos obtenidos de la aplicación del método en pruebas experimentales. En cuanto a la metodología aplicada, se empleó el método inductivo, enfoque cuantitativo, y un diseño de experimental, lo cual permitió llevar a cabo un estudio experimental en el cual se emplearon 13 simulaciones de comparaciones faciales, las cuales fueron proporcionadas a cada uno de los 09 observadores expertos, obteniendo un total de 117 pruebas experimentales, las cuales fueron clasificadas en aciertos (verdaderos positivos y negativos) y errores (falsos negativos y positivos). Seguidamente, los aciertos y errores fueron clasificados en una matriz de confusión, la cual permitió calcular los valores de precisión, exactitud, tasas de falsos positivos y negativos obtenidos a partir de la aplicación del método morfológico-comparativo