A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets

Blanton, Austin; Allen, Kristen; Miller, Tim; Kalka, Nathan D.; Jain, Anil K.

doi:10.1109/cvprw.2016.35

Cited by 16 publications

(10 citation statements)

References 19 publications

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“…We tested the recognition of images in different lighting environments with a classical face recognition algorithm (FRA-A) based on a Light CNN-9 [46] with max-feature-map (MFM) units. We know that the human visual system is very accurate at recognizing people, as it is even better than current state-ofthe-art recognition systems [47,48]. Similarly, some studies [5,31,49] have verified the usability of the human visual system in FIQA.…”

Section: Brightness and Sharpness Factor Verificationmentioning

confidence: 97%

Sharpness and Brightness Quality Assessment of Face Images for Recognition

Chen

Huang

et al. 2021

Scientific Programming

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Face image quality has an important effect on recognition performance. Recognition-oriented face image quality assessment is particularly necessary for the screening or application of face images with various qualities. In this work, sharpness and brightness were mainly assessed by a classification model. We selected very high-quality images of each subject and established nine kinds of quality labels that are related to recognition performance by utilizing a combination of face recognition algorithms, the human vision system, and a traditional brightness calculation method. Experiments were conducted on a custom dataset and the CMU multi-PIE face database for training and testing and on Labeled Faces in the Wild for cross-validation. The experimental results show that the proposed method can effectively reduce the false nonmatch rate by removing the low-quality face images identified by the classification model and vice versa. This method is even effective for face recognition algorithms that are not involved in label creation and whose training data are nonhomologous to the training set of our quality assessment model. The results show that the proposed method can distinguish images of different qualities with reasonable accuracy and is consistent with subjective human evaluation. The quality labels established in this paper are closely related to the recognition performance and exhibit good generalization to other recognition algorithms. Our method can be used to reject low-quality images to improve the recognition rate and screen high-quality images for subsequent processing.

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Section: Brightness and Sharpness Factor Verificationmentioning

confidence: 97%

Sharpness and Brightness Quality Assessment of Face Images for Recognition

Chen

Huang

et al. 2021

Scientific Programming

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“…It is important to note that algorithms demonstrate several patterns of image bias. For example, identification accuracy of male faces is often higher than identification accuracy for female faces (e.g., Blanton, Allen, Miller, Kalka, & Jain, 2016). Additionally, Phillips et al (2011) report that an 'other-race effect' for both humans and early algorithms.…”

Section: Algorithm Biasmentioning

confidence: 99%

Automatic Recognition Systems and Human Computer Interaction in Face Matching

Noyes¹,

Hill²

2021

Forensic Face Matching

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The human face facilitates identification in security and policing scenarios. In these settings, automatic face recognition systems have increased in prevalence and accuracy in recent years. As a result, the identification task, which once fell entirely to humans, is now a process performed by man and machine. Automatic face recognition systems provide image similarity comparisons and can create candidate lists to narrow down potential targets. There is increasing interest in the accuracy of these systems, and the role that algorithms can play in the identification effort. The design, operational usage, and effectiveness of these automatic systems, as well as the interaction of human and computer recognition are the topics of this chapter.

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“…Unsupervised methods do not require training data and therefore can be applied in very large datasets without the need for data annotation. Supervised methods have demonstrated exceptional performance in medical and biomedical applications, overperforming their unsupervised and data driven counterparts [4]. While they provide practical solutions in small datasets, scaling fully supervised algorithms to very large and heterogenous datasets is extremely difficult due to the large amount of annotated training data that human experts must provide [5].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform

Santamaría-Pang

Sood

Meyer

et al. 2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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We present a method for automatic cell classification in tissue samples using an automated training set from multiplexed immunofluorescence images. The method utilizes multiple markers stained in situ on a single tissue section on a robust hyperplex immunofluorescence platform (Cell DIVE TM , GE Healthcare) that provides multi-channel images allowing analysis at single cell/sub-cellular levels. The cell classification method consists of two steps: first, an automated training set from every image is generated using marker-to-cell staining information. This mimics how a pathologist would select samples from a very large cohort at the image level. In the second step, a probability model is inferred from the automated training set. The probabilistic model captures staining patterns in mutually exclusive cell types and builds a single probability model for the data cohort. We have evaluated the proposed approach to classify: i) immune cells in cancer and ii) brain cells in neurological degenerative diseased tissue with average accuracies above 95%.

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A Comparison of Human and Automated Face Verification Accuracy on Unconstrained Image Sets

Cited by 16 publications

References 19 publications

Sharpness and Brightness Quality Assessment of Face Images for Recognition

Sharpness and Brightness Quality Assessment of Face Images for Recognition

Automatic Recognition Systems and Human Computer Interaction in Face Matching

Automated Phenotyping via Cell Auto Training (CAT) on the Cell DIVE Platform

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