In India a countless number of children reported missing every year. Among the miming child cases a large percentage of children remain untraced. This paper presents a novel use of deep learning methodology for identifying the reported miming child from th e photos of multitude of children available, with the help of face recognition. The public can upload photographs of malicious child into s 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 fro m the missing child image database provided, using the facial image uploaded by the public. The Convohitional Neural Network (CNN), a highly effective deep learning technique for image based applications is adopted here for face recognition Face descriptor s are extracted from the images using a CNN model VGG Face deep architecture. Compared with normal deep learning applications, our algorithmsuses convolution network only as a high level feature extractor and the child recognition is done by the trained SV M classifier Choosing the best performing CNN model for face recognition, VOG 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 mining child identification. The classification performance achieved for child identification system is 99,41%. It was evaluated on 43 Child cares

In India a countless number of children reported missing every year. Among the miming child cases a large percentage of children remain untraced. This paper presents a novel use of deep learning methodology for