Missing Child Identification System Using Deep Learning And Multiclass SVM

Abstract

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