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1. FACIAL GRID TRANSFORMATION: A NOVEL FACE REGISTRATION APPROACH FOR IMPROVING FACIAL ACTION UNIT RECOGNITION

   https://doi.org/10.1109/ICIP.2014.7025283  

   Han, Shizhong; Meng, Zibo; Liu, Ping; Tong, Yan (October 2014, Proceedings - International Conference on Image Processing)

   Face registration is a major and critical step for face analysis. Existing facial activity recognition systems often employ coarse face alignment based on a few fiducial points such as eyes and extract features from equal-sized grid. Such extracted features are susceptible to variations in face pose, facial deformation, and person-specific geometry. In this work, we propose a novel face registration method named facial grid transformation to improve feature extraction for recognizing facial Action Units (AUs). Based on the transformed grid, novel grid edge features are developed to capture local facial motions related to AUs. Extensive experiments on two wellknown AU-coded databases have demonstrated that the proposed method yields significant improvements over the methods based on equal-sized grid on both posed and more importantly, spontaneous facial displays. Furthermore, the proposed method also outperforms the state-of-the-art methods using either coarse alignment or mesh-based face registration. 

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2. Facial Expression Recognition via a Boosted Deep Belief Network

   https://doi.org/10.1109/CVPR.2014.233  

   Ping Liu, Shizhong Han (June 2014, IEEE Conference on Computer Vision and Pattern Recognition)

   A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis. 

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3. Development of a Facial Feature Based Image Steganography Technology

   https://doi.org/10.1109/CSCI49370.2019.00126  

   Marella, Pranay; Straub, Jeremy; Bernard, Benjamin (December 2019, Proceedings of the 2019 International Conference on Computational Science and Computational Intelligence (CSCI))

   A new image steganography method is proposed for data hiding. This method uses least significant bit (LSB) insertion to hide a message in one of the facial features of a given image. The proposed technique chooses an image of a face from a dataset of 8-bit color images of head poses and performs facial recognition on the image to extract the Cartesian coordinates of the eyes, mouth, and nose. A facial feature is chosen at random and each bit of the binary representation of the message is hidden at the least significant bit in the pixels of the chosen facial feature. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. 

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4. Face Recognition Using Popular Deep Net Architectures: A Brief Comparative Study

   https://doi.org/10.3390/fi13070164  

   Gwyn, Tony; Roy, Kaushik; Atay, Mustafa (July 2021, Future Internet)

   null (Ed.)

   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. 

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5. Feature Disentangling Machine - A Novel Approach of Feature Selection and Disentangling in Facial Expression Analysis

   https://doi.org/10.1007/978-3-319-10593-2_11  

   Ping Liu, Joey Tianyi (September 2014, Lecture notes in computer science)

   Studies in psychology show that not all facial regions are of importance in recognizing facial expressions and different facial regions make different contributions in various facial expressions. Motivated by this, a novel framework, named Feature Disentangling Machine (FDM), is proposed to effectively select active features characterizing facial expressions. More importantly, the FDM aims to disentangle these selected features into non-overlapped groups, in particular, common features that are shared across different expressions and expression-specific features that are discriminative only for a target expression. Specifically, the FDM integrates sparse support vector machine and multi-task learning in a unified framework, where a novel loss function and a set of constraints are formulated to precisely control the sparsity and naturally disentangle active features. Extensive experiments on two well-known facial expression databases have demonstrated that the FDM outperforms the state-of-the-art methods for facial expression analysis. More importantly, the FDM achieves an impressive performance in a cross-database validation, which demonstrates the generalization capability of the selected features. 

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