Newborn face recognition using deep convolutional neural network Rishav Singh 1 & Hari Om 2 Received: 2 October 2016 /Revised: 12 December 2016 /Accepted: 30 December 2016 # Springer Science+Business Media New York 2017 Abstract Development of expertise in Face Recognition has led researchers to apply its various techniques for newborn recognition as some of the problems such as swapping, kidnapping are still prevalent. The paper proposes to apply Deep Convolutional Neural Network(CNN) to IIT(BHU) newborn database. The database has its own advantages where the quality of images is high and segregation has been done for various expressions of newborn. The Deep CNN applied in this paper is more advantageous when compared to regular MLP. Along with this the results taken from application of proposed technique have been compared to state-of-the-art technique applied on the same database and it shows improved results. It has been found Deep CNN improves PCA by 22.09%, LDA by 12.98%, ICA by 11.35%, LBP by 17.08% and SURF by 10.8% for Neutral-Neutral faces. Along with this results have also been gathered to understand which Deep CNN architecture is most suitable for the database. The CNN architecture with 2 convolutional layers and 1 hidden layer is the best solution. The results have also been cross validated using 10-fold cross validation. Keywords Face recognition . Deep convolutional neural network . Newborn 1 Introduction The animal visual cortex being the most powerful visual processing system in existence, it seems natural to emulate its behavior. From Hubel and Wiesel’ s early work on the cat’ s visual cortex, it is known that the visual cortex contains a complex arrangement of cells. These cells are sensitive to small sub-regions of the visual field, called a receptive field. The sub-regions are tiled to cover the Multimed Tools Appl DOI 10.1007/s11042-016-4342-x * Rishav Singh rishvsingh559@gmail.com Hari Om hariom4india@gmail.com 1 Education & Research, Infosys Ltd., Chandigarh, India 2 Computer Science Department, Indian School of Mines, Dhanbad, Jharkhand, India