Science上发表我校邓伟洪、郭军、胡佳妮、张洪刚等师生撰写的论

太牛了！！！

敢于挑战权威，很赞~！

Technical Comments Response to Comment on "100% Accuracy in Automatic Face Recognition" R. Jenkins* and A. M. Burton Contrary to the suggestion of Deng et al., image registration reduced face-recognition accuracy when divorced from the averaging procedure. Average-to-photo mapping generalizes beyond specific photographs, and averaging either gallery images or probe images can improve the match. The alternative protocol suggested by the authors is unsuitable because it evaluates face-matching algorithms, not face representations, and relies on standard image sets. Department of Psychology, University of Glasgow, Glasgow G12 8QQ, UK. * To whom correspondence should be addressed. E-mail: rob@psy.gla.ac.uk We reported that the process of image averaging can dramatically boost automatic face recognition (1). Deng et al. (2) suggest that image registration alone might improve face-recognition performance, and we tested this suggestion. Because the MyHeritage database (3) is constantly expanding, we first re-submitted the photographs and average images used in (1) to establish a current baseline. Forty-eight of the 500 probe images were identical to images in the online gallery, compared with 41 in (1). This increase is consistent with gallery expansion. Of the remaining 452 photographs, 52% were correctly identified, down from 54% in (1). The hit rate for the average images was 100%, as before. Five of the average images matched different photos of the correct person, confirming that the average-to-photo mapping generalizes beyond particular snapshots. To address Deng et al.'s concern, we next submitted manually registered versions of the source photographs. As Deng et al. describe, these were aligned in a standard frontal and upright posture and enclosed by a uniform background. The hit rate for the registered images was 30%. Apparently, registration alone offers the worst of both worlds: It disrupts any informative correspondence in shape between gallery and probe items but does not otherwise stabilize image variability. Registration of the probe images might be less harmful when the gallery images are also registered. In a previous study using a principal components analysis–based image match (4), we carried out exactly this transformation. Performance was poor but was nonetheless improved by averaging. Deng et al. (2) also express concern that our average images were presented as probes rather than being gallery items. This was a consequence of our chosen methodology. To ensure a stringent test of our averaging technique, we relinquished control over several key aspects of the image match. We used someone else's gallery photographs together with someone else's matching algorithm. Our probe images were collected from the Internet. This approach meant that we were not able to add images to the gallery, but we could still submit images as probes. Because face recognition can be reduced to matching pairs of images, the order of each pair was not our main interest, and we treated matching A to B as equivalent to matching B to A. In previous studies, we have shown that averaging also helps when applied to the gallery images (4). Whether identity checks would be better served by an average image stored in an identification document or an average probe generated from the live face is an interesting empirical question. However, it is worth pointing out that averaging probe images specifically finds practical application in forensic face recognition (5). Deng et al. point out that an average probe need only match one gallery photo of the target to score a hit. The same is true for the photographic probes, yet these performed comparatively poorly. In practice, an average probe can match very different photos of the target, as our new data confirm. This underscores the major benefit of averaging. Matching pairs of photos is extremely difficult, because both items contain information that is not diagnostic of identity. Matching a photo to an average is helpful because it eliminates non-diagnostic information from one item in the pair. There is no doubt that difficulties can still arise in this situation, but this is partly because the pair still includes a photograph. Our response is therefore not to retreat to matching pairs of photos but rather to investigate ways to eliminate photos from the match altogether. Matching pairs of average images is an obvious route to explore, and we are testing this possibility. Deng et al. recommend the Face Recognition Vendor Test (FRVT) (6) as a methodological template. This is unsuitable for several reasons. First, the FRVT evaluations compare performance of different matching algorithms on standard images. Our proposal concerns the representation of the face and is independent of the matching algorithm. Second, the standard databases consist of posed photographs, which grossly underrepresent the variability of ambient face images. Third, reliance on any standard database carries the risk of solving "database recognition" without tackling face recognition. The real world presents different crowds on different days, and systems aspiring to real-world application cannot ignore this inconvenience. Finally, we agree with Deng et al. that early processing and automatic feature extraction are interesting problems, but they are clearly separate from the problem of face recognition. To convince yourself of this, note that it is easy to locate landmarks on a face you cannot recognize and that doing so does not trigger identification. References and Notes 1. R. Jenkins, A. M. Burton, Science 319, 435 (2008).[Abstract/Free Full Text] 2. W. Deng, J. Guo, J. Hu, H. Zhang, Science 321, 912 (2008); www.sciencemag.org/cgi/content/full/321/5891/912c. 3. MyHeritage, www.myheritage.com. 4. A. M. Burton, R. Jenkins, P. J. B. Hancock, D. White, Cognit. Psychol. 51, 256 (2005). [CrossRef] [ISI] [Medline] 5. V. Bruce, H. Ness, P. J. B. Hancock, C. Newman, J. Rarity, J. Appl. Psychol. 87, 894 (2002). [CrossRef] [ISI] [Medline] 6. Face Recognition Vendor Test, www.frvt.org. Received for publication 21 April 2008. Accepted for publication 16 July 2008. http://www.sciencemag.org/cgi/content/full/321/5891/912d --------------------------------------------------------------------------------

大师兄金流弊。。。

嗯~不容易呀，这个要顶

nb大了~

第四作者... 【 在 win520 (winwin) 的大作中提到: 】 : 2008年1月25日，美国Science周刊发表了英国学者R. Jenkins和A. M. Burton的文章，提出了一种基于image averaging的人脸识别算法，在对25位世界名人的500幅照片的识别中，取得了令人震惊的100%的识别率，并提出了将该方法用于护照等证件照识别的建议。 : 我校在学博士生邓伟洪敏锐地意识到该文章存在问题，经过与导师郭军等人讨论和分析，找到了文章的问题所在，并迅速写出了一篇技术评论。Science杂志经过对原文作者进行置疑，确认了邓伟洪、郭军等人的观点，在2008年8月15日的周刊上发表了这篇评论，指出了原文中的错 : 据悉，这是我校师生首次在Science上发表论文，是一次历史性的突破。显示出北邮人攀登世界科技高峰、建设世界高水平大学的信心和勇气，标志着新一代北邮人引领学术、挑战权威的英雄气概。 : ...................

【 在 PtwCJ 的大作中提到: 】 : 第四作者... 。。。邓师兄的老师嘛

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