【摘要】 从手机解锁、小区门禁到餐厅吃饭、超市收银,再到高铁进站、机场安检以及医院看病,人脸、虹膜和指纹等生物特征已成为人们进入万物互联世界的数字身份证。生物特征识别赋予机器自动探测、捕获、处理、分析和识别数字化生理或行为信号的高级智能,是一个典型而又复杂的模式识别问题,一直处于人工智能技术发展前沿,在新一代人工智能规划、"互联网+"行动计划等国家战略中具有重要地位。由于生物特征识别涉及公众利益攸关的隐私、道德和法律等问题,近期也引起了广泛的社会关注。本文系统综述了生物特征识别学科发展现状、新兴方向、存在问题和可行思路,深入梳理了人脸、虹膜、指纹、掌纹、静脉、声纹、步态、行人重识别以及多模态融合识别的研究进展,以人脸为例重点介绍了生物特征识别领域近些年受到关注的新方向——对抗攻击和防御、深度伪造和反伪造,最后剖析总结了生物特征识别领域存在的3大挑战问题——"感知盲区"、"决策误区"和"安全红区"。本文认为必须变革和创新生物特征的传感、认知和安全机制,才有可能取得复杂场景生物识别学术研究和技术应用的根本性突破,破除现有生物识别技术的弊端,朝着"可感"、"可知"和"可信"的新一代生物特征识别总体目标发展。更多还原

【Abstract】 Biometrics, such as face, iris, and fingerprint recognition, have become digital identity proof for people to enter the "Internet of Everything". For example, one may be asked to present the biometric identifier for unlocking mobile phones, passing access control at airports, rail stations, and paying at supermarkets or restaurants. Biometric recognition empowers a machine to automatically detect, capture, process, analyze, and recognize digital physiological or behavioral signals with advanced intelligence. Thus, biometrics requires interdisciplinary research of science and technology involving optical engineering, mechanical engineering, electronic engineering, machine learning, pattern recognition, computer vision, digital image processing, signal analysis, cognitive science, neuroscience, human-computer interaction, and information security. Biometrics is a typical and complex pattern recognition problem, which is a frontier research direction of artificial intelligence. In addition, biometric identification is a key development area of Chinese strategies, such as the Development Plan on the New Generation of Artificial Intelligence and the "Internet Plus" Action Plan. The development of biometric identification involves public interest, privacy, ethics, and law issues; thus, it has also attracted widespread attention from the society. This article systematically reviews the development status, emerging directions, existing problems, and feasible ideas of biometrics and comprehensively summarizes the research progress of face, iris, fingerprint, palm print, finger/palm vein, voiceprint, gait recognition, person reidentification, and multimodal biometric fusion. The overview of face recognition includes face detection, facial landmark localization, 2 D face feature extraction and recognition, 3 D face feature extraction and recognition, facial liveness detection, and face video based biological signal measurement. The overview of iris recognition includes iris image acquisition, iris segmentation and localization, iris liveness detection, iris image quality assessment, iris feature extraction, heterogeneous iris recognition, fusion of iris and other modalities, security problems of iris biometrics, and future trends of iris recognition. The overview of fingerprint recognition includes latent fingerprint recognition, fingerprint liveness detection, distorted fingerprint recognition, 3 D fingerprint capturing, and challenges and trends of fingerprint biometrics. The overview of palm print recognition mainly introduces databases, feature models, matching strategies, and open problems of palm print biometrics. The overview of vein biometrics introduces main datasets and algorithms for finger vein, dorsal hand vein, and palm vein, and then points out the remaining unsolved problems and development trend of vein recognition. The overview of gait recognition introduces model-based and model-free methods for gait feature extraction and matching. The overview of person reidentification introduces research progress of new methods under supervised, unsupervised and weakly supervised conditions, gait database virtualization, generative gait models, and new problems, such as clothes changing, black clothes, and partial occlusions. The overview of voiceprint recognition introduces the history of speaker recognition, robustness of voiceprint, spoofing attacks, and antispoofing methods. The overview of multibiometrics introduces image-level, feature-level, score-level, and decision-level information fusion methods and deep learning based fusion approaches. Taking face as the exemplar biometric modality, new research directions that have received great attentions in the field of biometric recognition in recent years, i.e., adversarial attack and defense as well as Deepfake and anti-Deepfake, are also introduced. Finally, we analyze and summarize the three major challenges in the field of biometric recognition——"the blind spot of biometric sensors", "the decision errors of biometric algorithms" and "the red zone of biometric security". Therefore, the sensing, cognition, and security mechanisms of biometrics are necessary to achieve a fundamental breakthrough in the academic research and technologies applications of biometrics in complex scenarios to address the shortcomings of the existing biometric technologies and to move towards the overall goal of developing a new generation of "perceptible", "robust", and "trustworthy" biometric identification technology.更多还原