Longitudinal Study of Child Face Recognition

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

Age Invariant Face Recognition using Convolutional Neural Network

In the recent years, face recognition across aging has become very popular and challenging task in the area of face recognition.  Many researchers have contributed in this area, but still there is a significant gap to fill in. Selection of feature extraction and classification algorithms plays an important role in this area. Deep Learning with Convolutional Neural Networks provides us a combination of feature extraction and classification in a single structure. In this paper, we have presented a novel idea of 7-Layer CNN architecture for solving the problem of aging for recognizing facial images across aging. We have done extensive experimentations to test the performance of the proposed system using two standard datasets FGNET and MORPH(Album II). Rank-1 recognition accuracy of our proposed system is 76.6% on FGNET and 92.5% on MORPH(Album II). Experimental results show the significant improvement over available state-of- the-arts with the proposed CNN architecture and the classifier

Authentications of Myanmar National Registration Card

The automatic identification system of Myanmar national registration card (NRC) holder is presented in this paper. The proposed system can be handled the identification by the extracted low quality face image and fingerprint image from Myanmar NRC. Both of the facial recognition and fingerprint recognition system are developed for Myanmar citizenship confirmation. Age invariant face recognition algorithm is performed based on combination of DiaPCA (Diagonal principal Component Analysis) and KNN (Kth nearest neighbor classifier) approaches. An algorithm of the fingerprint recognition is proposed for recognition of the poor quality fingerprint image with fabric background.  Several experiments have been done for confirming the effectiveness of the proposed approach

A Method Based on Active Appearance Model and Gradient Orientation Pyramid of Face Verification as People Age

Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we propose to use the active appearance model (AAM) and gradient orientation pyramid (GOP) feature representation for this problem. First, we use the AAM on the dataset and generate the AAM images; we then get the representation of gradient orientation on a hierarchical model, which is the appearance of GOP. When combined with a support vector machine (SVM), experimental results show that our approach has excellent performance on two public domain face aging datasets: FGNET and MORPH. Second, we compare the performance of the proposed methods with a number of related face verification methods; the results show that the new approach is more robust and performs better