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face recognition accuracy of forensic examiners, superrecognizers, and face recognition algorithms

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2018年第24期115卷 6171-6176页

作者： Phillips, P. Jonathon Yates, Amy N. Hu, Ying Hahn, Carina A. Noyes, Eilidh Jackson, Kelsey Cavazos, Jacqueline G. Jeckeln, Geraldine Ranjan, Rajeev Sankaranarayanan, Swami Chen, Jun-Cheng Castillo, Carlos D. Chellappa, Rama White, David O'Toole, Alice J. NIST Informat Access Div Gaithersburg MD 20899 USA Univ Texas Dallas Sch Behav & Brain Sci Richardson TX 75080 USA Univ Maryland Inst Adv Comp Studies Dept Elect & Comp Engn College Pk MD 20854 USA Univ Maryland Inst Adv Comp Studies College Pk MD 20854 USA Univ New South Wales Sch Psychol Sydney NSW 2052 Australia

Achieving the upper limits of face identification accuracy in forensic applications can minimize errors that have profound social and personal consequences. Although forensic examiners identify faces in these applications, systematic tests of their accuracy are rare. How can we achieve the most accurate face identification: using people and/or machines working alone or in collaboration? In a comprehensive comparison of face identification by humans and computers, we found that forensic facial examiners, facial reviewers, and superrecognizers were more accurate than fingerprint examiners and students on a challenging face identification test. Individual performance on the test varied widely. On the same test, four deep convolutional neural networks (DCNNs), developed between 2015 and 2017, identified faces within the range of human accuracy. Accuracy of the algorithms increased steadily over time, with the most recent DCNN scoring above the median of the forensic facial examiners. Using crowd-sourcing methods, we fused the judgments of multiple forensic facial examiners by averaging their rating-based identity judgments. Accuracy was substantially better for fused judgments than for individuals working alone. Fusion also served to stabilize performance, boosting the scores of lower-performing individuals and decreasing variability. Single forensic facial examiners fused with the best algorithm were more accurate than the combination of two examiners. Therefore, collaboration among humans and between humans and machines offers tangible benefits to face identification accuracy in important applications. These results offer an evidence-based roadmap for achieving the most accurate face identification possible.

关键词： face identification forensic science face recognition algorithm wisdom-of-crowds machine learning technology

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Fusing ChatGPT and Human Decisions in Unfamiliar face Matching

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APPLIED COGNITIVE PSYCHOLOGY 2025年第2期39卷

作者： Kramer, Robin S. S. Univ Lincoln Sch Psychol Sport Sci & Wellbeing Lincoln England

Unfamiliar face matching involves deciding whether two face images depict the same person or two different people. Individual performance can be error-prone but is improved by aggregating (fusing) the responses of participant pairs. With advances in automated facial recognition systems (AFR), fusing human and algorithm responses also leads to performance improvements over individuals working alone. In the current work, I investigated whether ChatGPT could serve as the algorithm in this fusion. Using a common face matching test, I found that the fusion of individual responses with those provided by ChatGPT increased performance in comparison with both individuals working alone and simulated participant pairs. This pattern of results was evident when participants responded either using a rating scale (Experiment 1) or with a binary decision and associated confidence (Experiment 2). Taken together, these findings demonstrate the potential utility of ChatGPT in daily identification contexts where state-of-the-art AFR may not be available.

关键词： aggregation ChatGPT face identification face matching face recognition algorithm fusion

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The sparse representation and smoothed L0 algorithm for face recognition

The sparse representation and smoothed L0 algorithm for face...

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International Conference on Wavelet Analysis and Pattern recognition (ICWAPR)

作者： Jun-Yevg Zeng Yi-Kui Zhai Jun-Yevg Gan School of Information Engineering Wuyi University Jiangmen China

ISBN: (纸本)9781467372251

The sparse representation based classification (SRC) can effectively improve the face recognition rate. Smoothed l 0 algorithm has much faster calculation speed and requires fewer measured values than the other sparse representation method. In this paper, the sparse representation and smoothed l 0 algorithm for face recognition are presented to improve the face recognition under various conditions such as face disguise, illumination and pose changes, etc. The experiments on the AR, Extended Yale B and FERET face database verify the effectiveness of the presented method. The experimental results show that the face recognition algorithm increases to a certain extent in terms of recognition robustness and time than the original SRC.

关键词： face recognition image classification image representation smoothing methods AR face database Extended Yale B face database FERET face database SRC face disguise face recognition algorithm illumination pose changes recognition robustness smoothed l0 algorithm sparse representation based classification face recognition Sparse representation l0 norm Facial recognition sparse representation face recognition algorithm SAFETY REVIEW COMMITTEE smoothing methods image representation sparse representation-based classification Image classification illumination Lighting

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Sparse representation-based face recognition against expression and illumination

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IET IMAGE PROCESSING 2018年第5期12卷 826-832页

作者： Su, Ya Liu, Zhe Wang, Mengyao Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China

face recognition technique has obtained great progress and excellent results on public data sets. However, traditional algorithms suffer from various changes such as illumination, expression, and misalignment in practical applications. To solve these problems, this study proposes a novel face recognition algorithm simultaneously resolves these challenges. The key idea is reducing the influence of illumination and expression through the aligning procedure. As a result, illumination, expression, and misalignment can be greatly ignored in the recognition procedure. The contributions of this study are two folds. (i) The construction of the shape constrained illumination pattern (SCIP), which models the illumination variation with robustness to expression change. (ii) SCIP-based face recognition algorithm which can deal with illumination, expression, and image misalignment simultaneously. Systematic evaluations conducted on public databases demonstrate that the proposed algorithm is robust to illumination, expression, and misalignment with better performance than state-of-the-art algorithms.

关键词： face recognition image representation sparse representation shape constrained illumination pattern SCIP illumination variation face recognition algorithm

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face recognition based on a two-view projective transformation using one sample per subject

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IET COMPUTER VISION 2012年第5期6卷 489-498页

作者： Kuo, C. -H. Lee, J. -D. Chang Gung Univ Dept Elect Engn Tao Yuan 33302 Taiwan Chung Chou Univ Sci & Technol Dept Elect Engn & Energy Technol Changhua 51003 Taiwan

In this study, the authors propose a novel face recognition algorithm based on geometric features to alleviate the one sample per subject problem, called the robust estimation system. Our application adopts both local and global information for robust estimation. The authors utilise the original images from the ORL and Yale databases for evaluation. The images of the FERET database are pre-processed to extract the pure face region and execute the affine transformation. The authors roughly divide the face images into four block images that are most significant for the face: left eye, right eye, nose and mouth. The feature extraction using magnitude of first-order gradients, based on geometric features, is ideal for estimating a single sample. While conducting the classification stage, local features are putatively matched before processing or the global random sample consensus robust estimation features, with the aim of identifying the fundamental matrix between two matched face images. Finally, similarity scores are calculated, and the candidate awarded the highest score is designated the correct subject. Experiments were implemented using the FERET, ORL and Yale databases to demonstrate the efficiency of the proposed method. The experimental results show that our algorithm greatly improves recognition performance compared with the existing methods.

关键词： Optimisation techniques geometric features ORL databases Other topics in statistics affine transforms robust estimation system Image recognition similarity scores first-order gradients gradient methods Integral transforms Yale databases recognition performance matrix algebra FERET database affine transformation global random sample consensus robust estimation features local information Algebra face recognition two-view projective transformation matched face images face recognition algorithm image classification feature extraction Computer vision and image processing techniques one sample per subject problem estimation theory global information fundamental matrix face region random processes block images classification stage

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Personalised face neutralisation based on subspace bilinear regression

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IET COMPUTER VISION 2014年第4期8卷 329-337页

作者： Chen, Ying Bai, Ruilin Hua, Chunjian Jiangnan Univ Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China Jiangnan Univ Sch Mech Engn Wuxi 214122 Peoples R China

Expression face neutralisation helps to improve the performance of expressive face recognition with one single neutral sample in gallery per subject. For learning-based expression neutralisation, the virtual neutral face totally relies on training samples, which removes person-specific characters from the neutralised face. Bilinear kernel rank reduced regression (BKRRR) algorithm is designed in a virtual subspace to simultaneously and efficiently generate both virtual expressive and neutral images from training samples. An expression mask is then established using grey and gradient differences of the two images. The test expression image is transformed to neutral template by piece-wise affine warp (PAW). Using the virtual BKRRR neutral image as source, the PAW image as destination and the area covered by expression mask as clone area, an image fusion strategy based on Poisson equation is then designed, which achieves virtual neutralised face image with person-specific characters preserved. From experiments on the CMU Multi-PIE databases, it could be observed that the neutral faces synthesised by the proposed method could effectively approximate the real ground truth expressive faces, and greatly improve the performance of classic face recognition algorithms on expression variant problems.

关键词： Galleries grey destination Poisson equation Virtual neutral surface Facial recognition Plasma arc welding Image Neutralization face recognition algorithm

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Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion

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IET IMAGE PROCESSING 2015年第1期9卷 12-21页

作者： Nikan, Soodeh Ahmadi, Majid Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

A local-based illumination insensitive face recognition algorithm is proposed which is the combination of image normalisation and illumination invariant descriptors. Illumination insensitive representation of image is obtained based on the ratio of gradient amplitude to the original image intensity and partitioned into smaller sub-blocks. Local phase quantisation and multi-scale local binary pattern, extract the sub-regions characteristics. Distance measurements of local nearest neighbour classifiers are fused at the score level to find the best match and decision-level fusion combines the results of two matching techniques. Entropy, class posterior probability and mutual information are utilised as the weights of fusion components. Simulation results on the YaleB, Extended YaleB, AR, Multi-PIE and FRGC databases show the improved performance of the proposed algorithm under severe illumination with low computational complexity and no reconstruction or training requirement.

关键词： image normalization FUSION Quantization Nearest neighbor IMAGE INTENSITY Optimal matching complexity classes binary pattern face recognition algorithm

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Use of matrix polar decomposition for illumination-tolerant face recognition in discrete cosine transform domain

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IET IMAGE PROCESSING 2011年第1期5卷 25-35页

作者： Ezoji, M. Faez, K. Amirkabir Univ Technol Tehran Polytech Dept Elect Engn Tehran Iran

In this study, an illumination-tolerant face recognition algorithm is proposed. This work highlights the significance of matrix polar decomposition for illumination-invariant face recognition. The proposed algorithm has two stages. In the first stage, the authors reduce the effect of illumination changes by weakening the discrete cosine transform coefficients of block intensities using a new designed quantisation table. In the second stage, the unitary factor of polar decomposition of the reconstructed image is used as a feature matrix. In the recognition phase, a novel indirect method for measuring the similarities in feature matrices is proposed. The nearest-neighbour rule is applied to the matching. The authors have performed some experiments on several databases to evaluate the proposed method in its different aspects. Experimental results on recognition demonstrate that this approach provides a suitable representation for illumination invariant face recognition.

关键词： Quantization Phase A Nearest neighbor Facial recognition Polar decomposition face recognition algorithm Database

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Performance Improvement of face recognition algorithms Using Occluded-Region Detection

Performance Improvement of Face Recognition Algorithms Using...

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IAPR International Conference on Biometrics

作者： Yuichiro Tajima Koichi Ito Takafumi Aoki Tomoki Hosoi Sei Nagashima Koji Kobayashi Graduate School of Information Sciences Tohoku University Azbil Corporation

ISBN: (纸本)9781479903115

Facial occlusions such as eyeglasses, hairs and beards decrease the performance of face recognition algorithms. To improve the performance of face recognition algorithms, this paper proposes a novel framework of face recognition combined with the occluded-region detection method. In this paper, we detect occluded regions using Fast-Weighted Principal Component Analysis (FW-PCA) and use the occluded regions as weights for matching face images. To demonstrate the effectiveness of the proposed framework, we use two face recognition algorithms: Local Binary Patterns (LBP) and Phase-Only Correlation (POC). Experimental evaluation using public face image databases indicates performance improvement of the face recognition algorithms for face images with natural and artificial occlusions.

关键词： face recognition algorithm performance boost visual databases Eyeglasses face face Facial recognition

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One sample per person face recognition via sparse representation

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IET SIGNAL PROCESSING 2016年第9期10卷 1126-1134页

作者： Zhang, Yan Peng, Hua Natl Digital Switching Syst Engn & Technol Res Ct 7 Jianxue St Zhengzhou Peoples R China Zhengzhou Univ Light Ind Sch Comp & Commun Engn Dept Informat & Commun Engn 11Bldg 57 Jianxue St Zhengzhou Peoples R China

One sample per person face recognition (OSPP) is a challenging problem in face recognition community. Lack of samples leads to performance deterioration. Extended sparse representation-based classifier (ESRC) demonstrates excellent performance on OSPP. However, because there are intra-class variant atoms in the dictionary of ESRC, the number of atoms in the dictionary is always large and it will spend a long time during recognition. In this study, the authors propose a new OSPP face recognition algorithm via sparse representation (OSPP-SR). A compressed dictionary and a new identification strategy are provided in OSPP-SR. It is proved theoretically and experimentally that OSPP-SR reaches better or similar performance but spends less time than ESRC. Experiments are conducted on three different databases (extended Yale face database B, AR database and FERET database) to show the validity of OSPP-SR. Images under clean and noise conditions are also tested to evaluate the robustness of OSPP-SR.

关键词： Atoms Facial recognition Dictionaries as Topic Database face recognition algorithm

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