728 research outputs found

    Longitudinal Study of Child Face Recognition

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    We present a longitudinal study of face recognition performance on Children Longitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects, in the age group [2, 18] years. Each subject has at least four face images acquired over a time span of up to six years. Face comparison scores are obtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source matcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A and FaceNet matchers. To improve the performance of the open-source FaceNet matcher for child face recognition, we were able to fine-tune it on an independent training set of 3,294 face images of 1,119 children in the age group [3, 18] years. Multilevel statistical models are fit to genuine comparison scores from the CLF dataset to determine the decrease in face recognition accuracy over time. Additionally, we analyze both the verification and open-set identification accuracies in order to evaluate state-of-the-art face recognition technology for tracing and identifying children lost at a young age as victims of child trafficking or abduction

    Automatic Kinship Verification in Unconstrained Faces using Deep Learning

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    Kinship verification has a number of applications such as organizing large collections of images and recognizing resemblances among humans. Identifying kinship relations has also garnered interest due to several potential applications in security and surveillance and organizing and tagging the enormous number of videos being uploaded on the Internet. This dissertation has a five-fold contribution where first, a study is conducted to gain insight into the kinship verification process used by humans. Besides this, two separate deep learning based methods are proposed to solve kinship verification in images and videos. Other contributions of this research include interlinking face verification with kinship verification and creation of two kinship databases to facilitate research in this field. WVU Kinship Database is created which consists of multiple images per subject to facilitate kinship verification research. Next, kinship video (KIVI) database of more than 500 individuals with variations due to illumination, pose, occlusion, ethnicity, and expression is collected for this research. It comprises a total of 355 true kin video pairs with over 250,000 still frames. In this dissertation, a human study is conducted to understand the capabilities of human mind and to identify the discriminatory areas of a face that facilitate kinship-cues. The visual stimuli presented to the participants determines their ability to recognize kin relationship using the whole face as well as specific facial regions. The effect of participant gender, age, and kin-relation pair of the stimulus is analyzed using quantitative measures such as accuracy, discriminability index d′, and perceptual information entropy. Next, utilizing the information obtained from the human study, a hierarchical Kinship Verification via Representation Learning (KVRL) framework is utilized to learn the representation of different face regions in an unsupervised manner. We propose a novel approach for feature representation termed as filtered contractive deep belief networks (fcDBN). The proposed feature representation encodes relational information present in images using filters and contractive regularization penalty. A compact representation of facial images of kin is extracted as the output from the learned model and a multi-layer neural network is utilized to verify the kin accurately. The results show that the proposed deep learning framework (KVRL-fcDBN) yields state-of-the-art kinship verification accuracy on the WVU Kinship database and on four existing benchmark datasets. Additionally, we propose a new deep learning framework for kinship verification in unconstrained videos using a novel Supervised Mixed Norm regularization Autoencoder (SMNAE). This new autoencoder formulation introduces class-specific sparsity in the weight matrix. The proposed three-stage SMNAE based kinship verification framework utilizes the learned spatio-temporal representation in the video frames for verifying kinship in a pair of videos. The effectiveness of the proposed framework is demonstrated on the KIVI database and six existing kinship databases. On the KIVI database, SMNAE yields videobased kinship verification accuracy of 83.18% which is at least 3.2% better than existing algorithms. The algorithm is also evaluated on six publicly available kinship databases and compared with best reported results. It is observed that the proposed SMNAE consistently yields best results on all the databases. Finally, we end by discussing the connections between face verification and kinship verification research. We explore the area of self-kinship which is age-invariant face recognition. Further, kinship information is used as a soft biometric modality to boost the performance of face verification via product of likelihood ratio and support vector machine based approaches. Using the proposed KVRL-fcDBN framework, an improvement of over 20% is observed in the performance of face verification. By addressing several problems of limited samples per kinship dataset, introducing real-world variations in unconstrained databases and designing two deep learning frameworks, this dissertation improves the understanding of kinship verification across humans and the performance of automated systems. The algorithms proposed in this research have been shown to outperform existing algorithms across six different kinship databases and has till date the best reported results in this field

    DOMAIN ADAPTION FOR UNCONSTRAINED FACE VERIFICATION AND IDENTIFICATION

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    Face recognition has been receiving consistent attention in computer vision community for over three decades. Although recent advances in deep convolutional neural networks (DCNNs) have pushed face recognition algorithms to surpass human performance in most controlled situations, the unconstrained face recognition performance is still far from satisfactory. This is mainly because the domain shift between training and test data is substantial when faces are captured under extreme pose, blur or other covariates variations. In this dissertation, we study the effects of covariates and present approaches of mitigating the domain mismatch to improve the performance of unconstrained face verification and identification. To study how covariates affect the performance of deep neural networks on the large-scale unconstrained face verification problem, we implement five state-of-the-art deep convolutional networks (DCNNs) and evaluate them on three challenging covariates datasets. In total, seven covariates are considered: pose (yaw and roll), age, facial hair, gender, indoor/outdoor, occlusion (nose and mouth visibility, and forehead visibility), and skin tone. Some of the results confirm and extend the findings of previous studies, while others are new findings that were rarely mentioned before or did not show consistent trends. In addition, we demonstrate that with the assistance of gender information, the quality of a pre-curated noisy large-scale face dataset can be further improved. Based on the results of this study, we propose four domain adaptation methods to alleviate the effects of covariates. First, since we find that pose is a key factor for performance degradation, we propose a metric learning method to alleviate the effects of pose on face verification performance. We learn a joint model for face and pose verification tasks and explicitly discourage information sharing between the identity and pose metrics. Specifically, we enforce an orthogonal regularization constraint on the learned projection matrices for the two tasks leading to making the identity metrics for face verification more pose-robust. Extensive experiments are conducted on three challenging unconstrained face datasets that show promising results compared to state-of-the-art methods. Second, to tackle the negative effects brought by image blur, we propose two approaches. The first approach is an incremental dictionary learning method to mitigate the distribution difference between sharp training data and blurred test data. Some blurred faces called supportive samples are selected, which are used for building more discriminative classification models and act as a bridge to connect the two domains. Second, we propose an unsupervised face deblurring approach based on disentangled representations. The disentanglement is achieved by splitting the content and blur features in a blurred image using content encoders and blur encoders. An adversarial loss is added on deblurred results to generate visually realistic faces. We conduct extensive experiments on two challenging face datasets that show promising results. Finally, apart from the effects of pose and blur, face verification performance also suffers from the generic domain mismatch between source and target faces. To tackle this problem, we propose a template adaptation method for template-based face verification. A template-specific metric is trained to adaptively learn the discriminative information between test templates and the negative training set, which contains subjects that are mutually exclusive to subjects in test templates. Extensive experiments on two challenging face verification datasets yield promising results compared to other competitive methods

    Gait recognition based on shape and motion analysis of silhouette contours

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    This paper presents a three-phase gait recognition method that analyses the spatio-temporal shape and dynamic motion (STS-DM) characteristics of a human subject’s silhouettes to identify the subject in the presence of most of the challenging factors that affect existing gait recognition systems. In phase 1, phase-weighted magnitude spectra of the Fourier descriptor of the silhouette contours at ten phases of a gait period are used to analyse the spatio-temporal changes of the subject’s shape. A component-based Fourier descriptor based on anatomical studies of human body is used to achieve robustness against shape variations caused by all common types of small carrying conditions with folded hands, at the subject’s back and in upright position. In phase 2, a full-body shape and motion analysis is performed by fitting ellipses to contour segments of ten phases of a gait period and using a histogram matching with Bhattacharyya distance of parameters of the ellipses as dissimilarity scores. In phase 3, dynamic time warping is used to analyse the angular rotation pattern of the subject’s leading knee with a consideration of arm-swing over a gait period to achieve identification that is invariant to walking speed, limited clothing variations, hair style changes and shadows under feet. The match scores generated in the three phases are fused using weight-based score-level fusion for robust identification in the presence of missing and distorted frames, and occlusion in the scene. Experimental analyses on various publicly available data sets show that STS-DM outperforms several state-of-the-art gait recognition methods
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