Browsing by Author "Bowyer, Kevin"

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    Face recognition via adaptive sparse representations of random patches
    (IEEE, 2014) Mery Quiroz, Domingo Arturo; Bowyer, Kevin
    Unconstrained face recognition is still an open problem, as state-of-the-art algorithms have not yet reached high recognition performance in real-world environments (e.g., crowd scenes at the Boston Marathon). This paper addresses this problem by proposing a new approach called Adaptive Sparse Representation of Random Patches (ASR+). In the learning stage, for each enrolled subject, a number of random patches are extracted from the subject's gallery images in order to construct representative dictionaries. In the testing stage, random test patches of the query image are extracted, and for each test patch a dictionary is built concatenating the `best' representative dictionary of each subject. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification (SRC) methodology. Finally, the query image is classified by patch voting. Thus, our approach is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size and distance from the camera. Experiments were carried out on five widely-used face databases. Results show that ASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature in many complex scenarios.
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    Fair Face Verification by Using Non-Sensitive Soft-Biometric Attributes
    (2022) Villalobos, Esteban; Mery, Domingo; Bowyer, Kevin
    Facial recognition has been shown to have different accuracy for different demographic groups. When setting a threshold to achieve a specific False Match Rate (FMR) on a mixed demographic impostor distribution, some demographic groups can experience a significantly worse FMR. To mitigate this, some authors have proposed to use demographic-specific thresholds. However, this can be impractical in an operational scenario, as it would either require users to report their demographic group or the system to predict the demographic group of each user. Both of these options can be deemed controversial since the demographic group is a sensitive attribute. Further, this approach requires listing the possible demographic groups, which can become controversial in itself. We show that a similar mitigation effect can be achieved using non-sensitive predicted soft-biometric attributes. These attributes are based on the appearance of the users (such as hairstyle, accessories, and facial geometry) rather than how the users self-identify. Our experiments use a set of 38 binary non-sensitive attributes from the MAAD-Face dataset. We report results on the Balanced Faces in the Wild dataset, which has a balanced number of identities by race and gender. We compare clustering-based and decision-tree-based strategies for selecting thresholds. We show that the proposed strategies can reduce differential outcomes in intersectional groups twice as effectively as using gender-specific thresholds and, in some cases, are also better than using race-specific thresholds.
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    On accuracy estimation and comparison of results in biometric research
    (IEEE, 2016) Mery Quiroz, Domingo Arturo; Zhao, Yuning; Bowyer, Kevin
    The estimated accuracy of an algorithm is the most important element of the typical biometrics research publication. Comparisons between algorithms are commonly made based on estimated accuracies reported in different publications. However, even when the same dataset is used in two publications, there is a very low frequency of the publications using the same protocol for estimating algorithm accuracy. Using the example problems of face recognition, expression recognition and gender classification, we show that the variation in estimated performance on the same dataset across different protocols can be enormous. Based on these results, we make recommendations for how to obtain performance estimates that allow reliable comparison between algorithms.