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

MORPHOLOGICAL EDGE DETECTION AND CORNER DETECTION ALGORITHM USING CHAIN-ENCODING

Abstract- Edges and corners are regions of interest where there is a sudden change in intensity. These features play an important role in object identification methods used in machine vision and image processing systems. This paper presents a novel method for edge and corner detection in images. The approach used here is extracting Edges of the input image using morphological operator and then sending it for Chain Encoding. We are proposing a new morphological edge detector which returns a one pixel thick m-connected binary boundary image. This is followed by our chain encoding method to detect corners on the extracted edges. The algorithm works on all types of images (i.e. binary, gray level and color images). Since the proposed methods are based on morphological operations, these are very simple, efficient and fast. Experimental results on a variety of images identified all the prominent edges and significant corners efficiently. Index Terms—Morphological operations, edge detection, corner detection, thresholding, neighborhoods etc. [1] 1