Benchmarking In-the-wild Soft Biometric Attribute Identification

The active involvement of different demographic entities, whether positive or negative, demands effective identification of diverse populations. One way to quickly perform this identification is by segregating individuals based on their soft biometric attributes such as race and gender. Further, the soft biometric recognition technology has the potential to enhance individual privacy by providing a less invasive identification mode than conventional biometric methods. For example, a specific disbursement of government benefits might not require the exact identity of a person but only soft biometric attributes for its actual distribution. Secondly, the correct identification of race and gender can restrict the identity search space in which we need to look for an imposter. However, despite tremendous literature on identity recognition, limited work has been done to identify soft biometric attributes in challenging evaluation settings accurately. Therefore, in this research, for the first time, we have performed extensive experiments to benchmark the effectiveness of pure convolutional networks and attention networks to identify gender and ethnicity in the in-distribution and out-of-distribution (OOD) settings. We employed diverse face datasets to benchmark our methods, including UTKFace and FairFace. In contrast to the general understanding that OOD images lead to poor performance, we observe significant performance in identifying soft biometric attributes, including race and gender classification.