Handbook of Face Recognition
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This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.
transformed observation is a noise-corrupted version of some still template in the gallery, the observation equation can be written as (13.3) where v t is observation noise at time t, whose distribution determines the observation likelihood p(z t ∣n t ,θ t ), and is a transformed version of the observation z t . This transformation could be geometric, photometric, or both. However, when confronting difficult scenarios, one should use a more sophisticated likelihood function as discussed
psychology and neuroscience literature , is yet another avenue that has been challenging to model mathematically and replicate algorithmically. Acknowledgements Supported by a MURI Grant N00014-08-1-0638 from the Office of Naval Research. The authors would like to thank Dr. Aswin Sankaranarayanan for helpful discussions related to Sect. 13.5. References 1. Aggarwal, G., Roy-Chowdhury, A., Chellappa, R.: A system identification approach for video-based face recognition. In: International
spectrum. The LCTF provides narrowband filters with a full width-at-half-maximum bandwidth of 7 nm. A maximum of 331 narrow-band multispectral images can be acquired by continuously tuning the LCTF. The aperture of the LCTF is 35 mm and the field of view is . A wide angle lens is mounted on the monochrome camera (Sony XC-75) and this is coupled with the LCTF through a hardware interconnection. The camera auto-gain is set to 0 dB in order to acquire raw data. The black current of the Sony XC-75 is
database size. Reducing the database size increases this chance. Moreover, in multi-stage comparison the first stage is typically the weakest stage in terms of accuracy. While the more accurate higher stages might have the discriminative power to rank a mate high the first stage may fail to propagate it to the higher stages. Like above, this depends on the database size. Thus, the chances for propagating a mate to the next stage are raised by demographic filtering. 25.4.6 Binning The objective
Germany (3)USAREUR, CMR 420, Box 2872, APO, AE 09036, USA Nicole A. Spaun Email: Nicole.Spaun@us.army.mil Abstract In this chapter, we will first explain the current means of comparing faces used by forensic science laboratories. It is a nonautomated process performed by forensic examiners and has been referred to as facial “photographic comparison” or forensic facial identification. Next, we will outline the innovative ways in which facial recognition systems are being used by the forensic