# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 1 Introduction  
In the past few years, the field of machine learning has undergone some major developments. One important advancement is a technique known as \"deep learning\" that aims to model the high-level data abstractions by employing deep networked architectures composed of multiple linear/non-linear transformations. Deep learning systems are intelligent systems that mimic the workings of a human brain in representing complex data from real-world scenarios, and help in making intelligent decisions. Deep learning, also known as deep structured learning or hierarchical learning, belongs to the family of machine learning methods which are based on understanding data representation. It has made a remarkable impact in computer vision performance previously unattainable on many tasks such as image classification and object detection. Deep learning is applied in research concerning graphical modeling, pattern recognition, signal processing [1], computer vision [2], speech recognition [3], language recognition [4, 5], audio recognition [6], and face recognition (FR) [7]. In biometrics,deep learning can be used to represent the unique biometric data and make improvements in the performance of many authentication and recognition systems. Face recognition (FR) technology is identified as an active area of research in recent years because of the rise in security demands and the potential of the technology in law enforcement and commercial use [7]. FR contains two operational modes. First, verification mode is known as one-to-one matching in biometrics. The verification operational mode is used to pick out a face out of many faces in a face database to find out whether the face details belong to a particular person. Second, the identification mode is known as one-to-many matching. Identification involves taking the individual and comparing their biometrics to a database of possible identities. FR technology consists of four stages, which include face detection, alignment, representation (facial feature extraction), and classification [8]. In the FR system, the main challenge is the feature representation scheme used to extract features, using the better method for representation, for a given biometric trait. Feature extraction is one of the most important steps for image classification. Extracting features means retaining the most important information, which is required for classification. There are many feature extraction procedures that have been proposed for use in a biometric system, including principal component analysis (PCA) [9], independent component analysis (ICA) [10], local binary patterns (LBP) [11], and the histogram method [12]. Recently, the typical feature extraction approach used FR is deep learning, especially the convolution neural network (CNN), which shows remarkable advantages [13]. There are different approaches for using the CNN. First is learning the model from scratch. In this case, the architecture of the pre-trained model is used and trained according to the dataset. Second is using transfer learning with features from pre-trained CNN, in cases where the dataset is large. Finally, CNN can be used via transfer learning by keeping the convolutional base in its original form and then using its outputs to feed the classifier. The pre-trained model is used as a fixed feature extraction mechanism in cases where the dataset is small, or when the problem is similar to the one to be classified [14]. The goal of this paper was to apply pre-trained convolution neural network (CNN) approaches for FR and classification accuracy by analysis of FR performance using the pre-trained CNN (AlexNet and ResNet-50 models) for extracting features, followed by support vector machine (SVM) [15], and then using transfer learning with CNN (AlexNet model) for both feature extraction and classification.