Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition

Two approaches are proposed for cross-pose face recognition, one is based on the 3D reconstruction of facial components and the other is based on the deep Convolutional Neural Network (CNN). Unlike most 3D approaches that consider holistic faces, the proposed approach considers 3D facial components. It segments a 2D gallery face into components, reconstructs the 3D surface for each component, and recognizes a probe face by component features. The segmentation is based on the landmarks located by a hierarchical algorithm that combines the Faster R-CNN for face detection and the Reduced Tree Structured Model for landmark localization. The core part of the CNN-based approach is a revised VGG network. We study the performances with different settings on the training set, including the synthesized data from 3D reconstruction, the real-life data from an in-the-wild database, and both types of data combined. We investigate the performances of the network when it is employed as a classifier or designed as a feature extractor. The two recognition approaches and the fast landmark localization are evaluated in extensive experiments, and compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table

Improving Detection of DeepFakes through Facial Region Analysis in Images

\ua9 2023 by the authors. In the evolving landscape of digital media, the discipline of media forensics, which encompasses the critical examination and authentication of digital images, videos, and audio recordings, has emerged as an area of paramount importance. This heightened significance is predominantly attributed to the burgeoning concerns surrounding the proliferation of DeepFakes, which are highly realistic and manipulated media content, often created using advanced artificial intelligence techniques. Such developments necessitate a profound understanding and advancement in media forensics to ensure the integrity of digital media in various domains. Current research endeavours are primarily directed towards addressing a common challenge observed in DeepFake datasets, which pertains to the issue of overfitting. Many suggested remedies centre around the application of data augmentation methods, with a frequently adopted strategy being the incorporation of random erasure or cutout. This method entails the random removal of sections from an image to introduce diversity and mitigate overfitting. Generating disparities between the altered and unaltered images serves to inhibit the model from excessively adapting itself to individual samples, thus leading to more favourable results. Nonetheless, the stochastic nature of this approach may inadvertently obscure facial regions that harbour vital information necessary for DeepFake detection. Due to the lack of guidelines on specific regions for cutout, most studies use a randomised approach. However, in recent research, face landmarks have been integrated to designate specific facial areas for removal, even though the selection remains somewhat random. Therefore, there is a need to acquire a more comprehensive insight into facial features and identify which regions hold more crucial data for the identification of DeepFakes. In this study, the investigation delves into the data conveyed by various facial components through the excision of distinct facial regions during the training of the model. The goal is to offer valuable insights to enhance forthcoming face removal techniques within DeepFake datasets, fostering a deeper comprehension among researchers and advancing the realm of DeepFake detection. Our study presents a novel method that uses face cutout techniques to improve understanding of key facial features crucial in DeepFake detection. Moreover, the method combats overfitting in DeepFake datasets by generating diverse images with these techniques, thereby enhancing model robustness. The developed methodology is validated against publicly available datasets like FF++ and Celeb-DFv2. Both face cutout groups surpassed the Baseline, indicating cutouts improve DeepFake detection. Face Cutout Group 2 excelled, with 91% accuracy on Celeb-DF and 86% on the compound dataset, suggesting external facial features’ significance in detection. The study found that eyes are most impactful and the nose is least in model performance. Future research could explore the augmentation policy’s effect on video-based DeepFake detection

Face Detection Using Single Cascade of Customized Features Discriminators

Face detection has become an important and helpful tool for camera and video processing. Useful human-computer interaction (HCI) applications such as drivers assistant system that prevents accidents and saves pedestrian lives when drivers attention is absent, needs a head pose estimator. A head pose estimator cannot function without face detector. There has been a considerable amount of literature to address the problem. The most significant results obtained on uptight frontal face detection which is a sub-problem of a larger problem of face detection. There are other types of sub-problems that has been studied with least significant advancements that the upright frontal face detection had accomplished. The problem of multi-pose detection is still under study and it remains hard. A solution to this large scale of the problem (multi-pose face detection) is critical in head pose accuracy. This thesis suggests a multi-pose face detection algorithm for uncontrolled environments. The detector is designed to be used in building head pose estimator for a human-computer interaction application. The observed design of the detector has to implement a cascade of classifiers. Each classifier has to address at least one certain area of the problem. The design have to maintain speed and an acceptable detection rate. These requirements can be satisfied by constructing the cascade to implement fast and simple classifiers at first stages of the cascade. A novel use of the integral image as a fast filter was invented to be placed at the start of the detection process. Included in the cascade, classifiers that are trained on special designed features aimed to solve part of the problem. One special unique classifier is a data mining based classifier that uses a modified version of the Maximal Frequent Itemset Algorithm (MAFIA) [2] for feature extraction. Special features classifiers use the extracted facial features information extracted from a new knowledge-based classifier/filter that was created with the capacity to locate to an acceptable ac- curacy the location of eyes, mouth and nose using a suite of approaches including discreet local minima and geometric measures. The extracted facial features were used to estimate head pose and extract classifier features accordingly to enhance detection rates. A cascade of classifiers based on fast and simple contrast features was used to refine and speed up the detection process. To further improve speed some components were parallelized. As an attempt to overcome some of the fundamental challenges of face detection, lighting correction and noise reduction were implemented based on the information extracted from images. Results are reported on the FDDB [12] benchmark showed 5.22% detection rate with 2000 false positives while OpenCV implementation of Viola-Jones [19] face detector showed 65.92 detection rate with 2010 false positives. This comparison is flawed; because Viola-Jones is an upright face detector and even though FDDB [12] includes a number on non-frontal faces and profiles the majority of the faces are frontal. The two solutions address two different problems that reflect large differences in difficulty. A standard benchmark testset and evaluation system as FDDB [12] benchmark and com- parable results from the same class of the problem at the time of writing this document was not available. The key points to building good face detector in general are; (1) resolving speed issues using fast techniques (e.g. integral image) at the start of the cascade and a powerful design, (2) using a huge number of different strong and weak features, and (3) eliminating variations (i.e. pose , noise and lighting variations). The algorithm was also tested on MIT+CMU upfront faces testset and reported 43.56% detection rate with 504 false positives

Complex networks for data-driven medicine: the case of Class III dentoskeletal disharmony

In the last decade, the availability of innovative algorithms derived from complexity theory has inspired the development of highly detailed models in various fields, including physics, biology, ecology, economy, and medicine. Due to the availability of novel and ever more sophisticated diagnostic procedures, all biomedical disciplines face the problem of using the increasing amount of information concerning each patient to improve diagnosis and prevention. In particular, in the discipline of orthodontics the current diagnostic approach based on clinical and radiographic data is problematic due to the complexity of craniofacial features and to the numerous interacting co-dependent skeletal and dentoalveolar components. In this study, we demonstrate the capability of computational methods such as network analysis and module detection to extract organizing principles in 70 patients with excessive mandibular skeletal protrusion with underbite, a condition known in orthodontics as Class III malocclusion. Our results could possibly constitute a template framework for organising the increasing amount of medical data available for patients' diagnosis

Complex networks for data-driven medicine: The case of Class III dentoskeletal disharmony

In the last decade, the availability of innovative algorithms derived from complexity theory has inspired the development of highly detailed models in various fields, including physics, biology, ecology, economy, and medicine. Due to the availability of novel and ever more sophisticated diagnostic procedures, all biomedical disciplines face the problem of using the increasing amount of information concerning each patient to improve diagnosis and prevention. In particular, in the discipline of orthodontics the current diagnostic approach based on clinical and radiographic data is problematic due to the complexity of craniofacial features and to the numerous interacting co-dependent skeletal and dentoalveolar components. In this study, we demonstrate the capability of computational methods such as network analysis and module detection to extract organizing principles in 70 patients with excessive mandibular skeletal protrusion with underbite, a condition known in orthodontics as Class III malocclusion. Our results could possibly constitute a template framework for organising the increasing amount of medical data available for patients' diagnosis