# Deep Convolutional Neural Network-Based Approaches for Face Recognition
## 3 Related Work
[45] proposed a system for face recognition using portable smart glasses based on CNN. The detection process was performed using Haar-like features. The method archived detection rate at 98% using 3099 features. They used transfer learning from AlexNet for trained CNN model. The experiments of the study were conducted using 2500 images in a class. The results of the study showed that the accuracy of the system proposed was 98.5%. [PERSON] et al. [46] proposed a recognition algorithm based on deep CNNs. The algorithm contained face detection, face alignment, and feature extraction. The deep CNNs VGG16 was used to extract facial features. The experiments used the images of five angles (left, right, front, overlook, and look up). The experiment results showed that the algorithm achieved well on recognizing faces for cases of various poses in an indoor environment. [PERSON] et al. [47] investigated two deep presentation processes composed of learning from CNN and weight adjustment, and iris spoof detection and fingerprints, the latter of which was the best approach for face detection and imaging. They admitted that indeed there was very limited experimental knowledge on the biometric spoofing at the sensors for deriving an outstandingly comprehensive spoofing detection framework for the face, iris, and fingerprint variations based on two major deep learning approaches. These approaches included a focus on learning of the weights of the networks through back propagation and learning of suitable convolutional network architectures for each of the CNN's domains. [PERSON] et al. [48] proposed a method on how to improve facial recognition. A multimodal facial recognition using the CNN's systems is a good approach to facial recognition. They fused the modality-specific CNNs with histograms of Gabor ordinal measures (HOGOMs), local binary patterns (LBP), histograms of oriented gradients, and Haar-like features. The result of the approach significantly reduced the recognition error rate. Using more sophisticated computer systems will improve the process of deep learning. Similarly, there has been research-applied CNN, but this has been used on newborn FR [41]. Another study by [PERSON] et al. [49] proposed VGG-Face system, which applied a 16-layer CNN trained on 2.6 million images and was shown to achieve even better results. [PERSON] et al. [50] used a deep network to \"warp\" faces into a canonical frontal view, after which the system learned CNN, which in turn classified the particular faces as those that matched a particular identity. For face verification, PCA on the network output in conjunction with an ensemble of SVMs was used. Also, [PERSON] et al. [51] proposed an FR system based on CNN for feature extraction and SVM as a classifier. In order to enhance the performance of CNN, they used techniques for optimization to be training CNN. The model spends less time for training and gains a high recognition rate. The experiments in the study were conducted on the basis of the FERET and ORL dataset. The results of the experiments showed the system obtained and demonstrated a high recognition rate and less training time. Even though CNNs have been used in FR technology dating back to 1997 [52], there are major improvements in that there are massive image datasets that are available and have revealed their power. A work used representatively for this approach is Deep-Face [13], whereby the researchers trained an eight-layer CNN architecture. These layers were distributed--the first three were conventional convolution-pooling-convolution layers, followed three layers that were locally connected and then two fully connected layers. It is crucial to note that the pooling layers had an effect of making learned features robust to local transformations but caused a miss in local texture details. The pooling layers were critical for object recognition because these objects were not properly aligned. It is, however, important to note that face images should be well-trained before CNN training.