MISSING CHILD DISTINGUISHING FRAMEWORK USING DEEP LEARNING AND MULTICLASS SVM

Abstract

This project presents a novel approach to assist law enforcement agencies and communities in locating missing children by leveraging deep learning and Support Vector Machines (SVM).SVM & deep learning for distinguishing and categorizing distinguishing features in images of missing children. We propose a robust framework that automates the process of extracting and analyzing distinguishing attributes like unique identifiers from images.Enabling faster and more accurate matching against potential sightings or unidentified individuals. Our model demonstrates promising results in distinguishing features extraction and classification, offering a valuable tool to aid in the critical task of locating missing children and reuniting them with their families.
In India a countless number of children are reported missing every year. Among the missing child cases a large percentage of children remain untraced. This paper presents a novel use of deep learning methodology for identifying the reported missing child from the photos of multitude of children available, with the help of face recognition. The public can upload photographs of suspicious child into a common portal with landmarks and remarks. The photo will be automatically compared with the registered photos of the missing child from the repository. Classification of the input child image is performed and photo with best match will be selected from the database of missing children. For this, a deep learning model is trained to correctly identify the missing child from the missing child image database provided, using the facial image uploaded by the public. The Convolutional Neural Network (CNN), a highly effective deep learning technique for image based applications is adopted here for face recognition. Face descriptors are extracted from the images using a pre-trained CNN model VGG-Face deep architecture. Compared with normal deep learning applications, our algorithm uses convolution network only as a high level feature extractor and the child recognition is done by the trained SVM classifier. Choosing the best performing CNN model for face recognition, VGG-Face and proper training of it results in a deep learning model invariant to noise, illumination, contrast, occlusion, image pose and age of the child and it outperforms earlier methods in face recognition based missing child identification. The classification performance achieved for child identification system is 99.41%. It was evaluated on 43 Child cases.