# Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study
###### Abstract  
In the realm of computer security, the username/password standard is becoming increasingly antiquated. Usage of the same username and password across various accounts can leave a user open to potential vulnerabilities. Authentication methods of the future need to maintain the ability to provide secure access without a reduction in speed. Facial recognition technologies are quickly becoming integral parts of user security, allowing for a secondary level of user authentication. Augmenting traditional username and password security with facial biometrics has already seen impressive results; however, studying these techniques is necessary to determine how effective these methods are within various parameters. A Convolutional Neural Network (CNN) is a powerful classification approach which is often used for image identification and verification. Quite recently, CNNs have shown great promise in the area of facial image recognition. The comparative study proposed in this paper offers an in-depth analysis of several state-of-the-art deep learning based-facial recognition technologies, to determine via accuracy and other metrics which of those are most effective. In our study, VGG-16 and VGG-19 showed the highest levels of image recognition accuracy, as well as F1-Score. The most favorable configurations of CNN should be documented as an effective way to potentially augment the current username/password standard by increasing the current method's security with additional facial biometrics. Convolutional Neural Networks; authentication; biometrics; face biometrics; facial recognition; classification methods +
Footnote †: journal: _Future Internet_  
defense against attacks [3], when deployed as a single system, their security as a standalone package is often lacking [4; 5]. If an intruder is able to attain access to a user's unique certificate or their computer, they can quickly compromise a single sign-on WebID system if it does not have any additional security measures [6; 7]. Conversely, WebIDs that have been enhanced with biometric authentication are harder to overcome, but this added security also increases the system's overall computational costs [8]. Additional schemas, including Eigenfaces [9; 10] and Fisherfaces [11; 12], work well at classifying images; however, their inability to handle the pace needed for real-time biometric authentication is an issue that can cause major problems when married to some authentication systems [13]. Various methods for facial recognition have also considered, including Artificial Neural Networks (ANNs) based on feed-forward classification. Several papers that use this type of classification indeed exist in the literature, including pattern recognition detection [14] and wavelet-based image classification [15]. In our research, it was determined that non-recurrent networks such as these, where information travels only in one direction, would be insufficient to the increasingly arduous process of facial image recognition [16]. The problem at hand is intricate and complex. As advances in technologies continue, and as attackers make use of such measures, the username/password standard is quickly becoming outdated. While several different attempts to increase the security of systems have been undertaken, those attempts have seen limited success. Further research into other methods, namely Convolutional Neural Networks (CNNs), should be investigated. CNNs are powerful texture classification schemas which have been introduced to the realm of facial recognition with great success [17]. While the accuracy of CNNs has been thoroughly studied in a multitude of papers and journals, to our best knowledge, an extensive analysis to determine the best CNN for facial recognition has not yet been undertaken. There are a multitude of CNN models available in the literature that have been used for facial recognition. These CNNs have various parameters that can be adjusted for the purpose of increasing accuracy with respect to facial recognition.