Learning from Millions of 3D Scans for Large-scale 3D Face Recognition

Deep networks trained on millions of facial images are believed to be closely approaching human-level performance in face recognition. However, open world face recognition still remains a challenge. Although, 3D face recognition has an inherent edge over its 2D counterpart, it has not benefited from the recent developments in deep learning due to the unavailability of large training as well as large test datasets. Recognition accuracies have already saturated on existing 3D face datasets due to their small gallery sizes. Unlike 2D photographs, 3D facial scans cannot be sourced from the web causing a bottleneck in the development of deep 3D face recognition networks and datasets. In this backdrop, we propose a method for generating a large corpus of labeled 3D face identities and their multiple instances for training and a protocol for merging the most challenging existing 3D datasets for testing. We also propose the first deep CNN model designed specifically for 3D face recognition and trained on 3.1 Million 3D facial scans of 100K identities. Our test dataset comprises 1,853 identities with a single 3D scan in the gallery and another 31K scans as probes, which is several orders of magnitude larger than existing ones. Without fine tuning on this dataset, our network already outperforms state of the art face recognition by over 10%. We fine tune our network on the gallery set to perform end-to-end large scale 3D face recognition which further improves accuracy. Finally, we show the efficacy of our method for the open world face recognition problem.Comment: 11 page

Biometric Spoofing: A JRC Case Study in 3D Face Recognition

Based on newly available and affordable off-the-shelf 3D sensing, processing and printing technologies, the JRC has conducted a comprehensive study on the feasibility of spoofing 3D and 2.5D face recognition systems with low-cost self-manufactured models and presents in this report a systematic and rigorous evaluation of the real risk posed by such attacking approach which has been complemented by a test campaign. The work accomplished and presented in this report, covers theories, methodologies, state of the art techniques, evaluation databases and also aims at providing an outlook into the future of this extremely active field of research.JRC.G.6-Digital Citizen Securit

Biometric antispoofing methods: A survey in face recognition

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. J. Galbally, S. Marcel and J. Fierrez, "Biometric Antispoofing Methods", IEEE Access, vol.2, pp. 1530-1552, Dec. 2014In recent decades, we have witnessed the evolution of biometric technology from the rst pioneering works in face and voice recognition to the current state of development wherein a wide spectrum of highly accurate systems may be found, ranging from largely deployed modalities, such as ngerprint, face, or iris, to more marginal ones, such as signature or hand. This path of technological evolution has naturally led to a critical issue that has only started to be addressed recently: the resistance of this rapidly emerging technology to external attacks and, in particular, to spoo ng. Spoo ng, referred to by the term presentation attack in current standards, is a purely biometric vulnerability that is not shared with other IT security solutions. It refers to the ability to fool a biometric system into recognizing an illegitimate user as a genuine one by means of presenting a synthetic forged version of the original biometric trait to the sensor. The entire biometric community, including researchers, developers, standardizing bodies, and vendors, has thrown itself into the challenging task of proposing and developing ef cient protection methods against this threat. The goal of this paper is to provide a comprehensive overview on the work that has been carried out over the last decade in the emerging eld of antispoo ng, with special attention to the mature and largely deployed face modality. The work covers theories, methodologies, state-of-the-art techniques, and evaluation databases and also aims at providing an outlook into the future of this very active eld of research.This work was supported in part by the CAM under Project S2009/TIC-1485, in part by the Ministry of Economy and Competitiveness through the Bio-Shield Project under Grant TEC2012-34881, in part by the TABULA RASA Project under Grant FP7-ICT-257289, in part by the BEAT Project under Grant FP7-SEC-284989 through the European Union, and in part by the Cátedra Universidad Autónoma de Madrid-Telefónica

A Survey on Ear Biometrics

Recognizing people by their ear has recently received significant attention in the literature. Several reasons account for this trend: first, ear recognition does not suffer from some problems associated with other non contact biometrics, such as face recognition; second, it is the most promising candidate for combination with the face in the context of multi-pose face recognition; and third, the ear can be used for human recognition in surveillance videos where the face may be occluded completely or in part. Further, the ear appears to degrade little with age. Even though, current ear detection and recognition systems have reached a certain level of maturity, their success is limited to controlled indoor conditions. In addition to variation in illumination, other open research problems include hair occlusion; earprint forensics; ear symmetry; ear classification; and ear individuality. This paper provides a detailed survey of research conducted in ear detection and recognition. It provides an up-to-date review of the existing literature revealing the current state-of-art for not only those who are working in this area but also for those who might exploit this new approach. Furthermore, it offers insights into some unsolved ear recognition problems as well as ear databases available for researchers

3D FACE RECOGNITION USING LOCAL FEATURE BASED METHODS

Face recognition has attracted many researchers’ attention compared to other biometrics due to its non-intrusive and friendly nature. Although several methods for 2D face recognition have been proposed so far, there are still some challenges related to the 2D face including illumination, pose variation, and facial expression. In the last few decades, 3D face research area has become more interesting since shape and geometry information are used to handle challenges from 2D faces. Existing algorithms for face recognition are divided into three different categories: holistic feature-based, local feature-based, and hybrid methods. According to the literature, local features have shown better performance relative to holistic feature-based methods under expression and occlusion challenges. In this dissertation, local feature-based methods for 3D face recognition have been studied and surveyed. In the survey, local methods are classified into three broad categories which consist of keypoint-based, curve-based, and local surface-based methods. Inspired by keypoint-based methods which are effective to handle partial occlusion, structural context descriptor on pyramidal shape maps and texture image has been proposed in a multimodal scheme. Score-level fusion is used to combine keypoints’ matching score in both texture and shape modalities. The survey shows local surface-based methods are efficient to handle facial expression. Accordingly, a local derivative pattern is introduced to extract distinct features from depth map in this work. In addition, the local derivative pattern is applied on surface normals. Most 3D face recognition algorithms are focused to utilize the depth information to detect and extract features. Compared to depth maps, surface normals of each point can determine the facial surface orientation, which provides an efficient facial surface representation to extract distinct features for recognition task. An Extreme Learning Machine (ELM)-based auto-encoder is used to make the feature space more discriminative. Expression and occlusion robust analysis using the information from the normal maps are investigated by dividing the facial region into patches. A novel hybrid classifier is proposed to combine Sparse Representation Classifier (SRC) and ELM classifier in a weighted scheme. The proposed algorithms have been evaluated on four widely used 3D face databases; FRGC, Bosphorus, Bu-3DFE, and 3D-TEC. The experimental results illustrate the effectiveness of the proposed approaches. The main contribution of this work lies in identification and analysis of effective local features and a classification method for improving 3D face recognition performance

Show, don't tell: Non-verbal eyewitness testimony based on non-artistic face sketches

Ph.DDOCTOR OF PHILOSOPH

Modeling of Facial Wrinkles for Applications in Computer Vision

International audienceAnalysis and modeling of aging human faces have been extensively studied in the past decade for applications in computer vision such as age estimation, age progression and face recognition across aging. Most of this research work is based on facial appearance and facial features such as face shape, geometry, location of landmarks and patch-based texture features. Despite the recent availability of higher resolution, high quality facial images, we do not find much work on the image analysis of local facial features such as wrinkles specifically. For the most part, modeling of facial skin texture, fine lines and wrinkles has been a focus in computer graphics research for photo-realistic rendering applications. In computer vision, very few aging related applications focus on such facial features. Where several survey papers can be found on facial aging analysis in computer vision, this chapter focuses specifically on the analysis of facial wrinkles in the context of several applications. Facial wrinkles can be categorized as subtle discontinuities or cracks in surrounding inhomogeneous skin texture and pose challenges to being detected/localized in images. First, we review commonly used image features to capture the intensity gradients caused by facial wrinkles and then present research in modeling and analysis of facial wrinkles as aging texture or curvilinear objects for different applications. The reviewed applications include localization or detection of wrinkles in facial images , incorporation of wrinkles for more realistic age progression, analysis for age estimation and inpainting/removal of wrinkles for facial retouching

Twin Studies: Research in Genes, Teeth and Faces

This volume is about an ongoing long-term research initiative led by researchers from the School of Dentistry at the University of Adelaide. The aim of this book is to provide an overview of the studies of the teeth and faces of Australian twins and their families that have extended over more than thirty years