(IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 9, No. 12, 2018 240 | Page www.ijacsa.thesai.org CNNSFR: A Convolutional Neural Network System for Face Detection and Recognition Lionel Landry SOP DEFFO 1 , Elie TAGNE FUTE 2 Department of Computer Engineering, Department of Mathematics and Computer Science, {University of Dschang, University of Buea} Cameroon Emmanuel TONYE 3 National Advanced School of Engineering, Department of Electrical Engineering {University of Yaounde I} Cameroon Abstract—In recent years, face recognition has become more and more appreciated and considered as one of the most promising applications in the field of image analysis. However, the existing models have a high level of complexity, use a lot of computational resources and need a lot of time to train the model. That is why it has become a promising field of research where new methods are being proposed every day to overcome these difficulties. We propose in this paper a convolutional neural network system for face recognition with some contributions. First we propose a CRelu module, second we use the module to propose a new architecture model based on the VGG deep neural network model. Thirdly we propose a two stage training strategy improved by a large margin inner product and a small dataset and finally we propose a real time face recognition system where face detection is done by a multi-cascade convolution neural network and the recognition is done by the proposed deep convolutional neural network. Keywords—Convolutional neural network; face recognition; VGG model; CReLU module; deep learning; architecture I. INTRODUCTION High-quality cameras in mobile devices have made facial recognition a viable option for authentication as well as identification. However, the used multimedia computational devices cannot act as well human being does. That is why studies have tried to mimic the behavior of human brain to approximate artificially the results obtained by a human being: it is the notion of deep learning. In the mid-1960s, scientists began work on using the computer to recognize human faces. Since then, facial recognition software has come a long way. In 1966, Bledsoe [1], [2] developed a system that could classify photos of faces by hand using what’s known as a RAND tablet, a device that people could use to input horizontal and vertical coordinates on a grid using a stylus that emitted electromagnetic pulses In 1987, Sirovich and Kriby [3], were able to show that feature analysis on a collection of facial images could form a set of basic features. They were also able to show that less than one hundred values were required in order to accurately code a normalized face. In 1991, Turk and Pentland [4] expanded upon the Eigen face approach by discovering how to detect faces within images. This led to the first instances of automatic face recognition From 1993 to 2000 the Defense Advanced Research Projects Agency (DARPA) and the National Institute of Standards and Technology rolled out the Face Recognition Technology (FERET) program [5] which consists of creating a database of facial images. The database was updated in 2003 to include high-resolution 24-bit color versions of images. Included in the test set were 2,413 still facial images representing 856 people. From 2005, the Face Recognition Grand Challenge (FRGC) [6] consisted of progressively difficult challenge problems was launched. It includes sufficient elements to overcome the lack of data. The set of defined experiments assists researchers and developers in making progress to meet the new performance goals. The year 2010 was marked with a great change in the social media platforms all over the world and has leaded researchers to develop photo tagging feature for its user. However the accuracy was not that satisfying that is why technologies using deep learning such as deep face where born [7]. His tools identify human faces in digital images. It employs a nine layer neural network with over 120 million connection weights and was trained on four million images uploaded by Facebook users. Many other models have been developed over years and two of the most popular are Facenet network [8] and VGG network [9]. They propose a deep architecture that is able to deal with the complexity of classification problem. However, these architectures generally need a very huge date set and a lot of iterations to have good results which if often difficult to have in some cases. This paper presents a convolutional Neural Network System for Face Recognition based on VGG model and has four proposed contributions. First we propose a CRelu module that has proved to be efficient in enhancing computations; second we use the module to propose a new architecture of VGG network. Thirdly we propose training strategy that needs small dataset and we prove that it leads to good results and finally we propose a real time face recognition system where face detection is done by a multi-cascade convolution neural network and the recognition is done by the proposed deep convolutional neural network. The rest of the paper is organized as follows: Section 2 presents the details on the proposed approach. In Section 3, the training methodology is presented. Section 4 presents the