Accenture-MM1: A Multimodal Person Recognition Dataset

Kyle O'Brien, Michelle Rybak, Jiong Huang, Adam Stevens, Madeline Fredriksz, Michael Chaberski, Danielle Russell, Lindsey Castin, Michelle Jou, Nishant Gurrapadi, Marc Bosch; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops, 2024, pp. 112-122


In this paper we present a new dataset to fuel multimodal research in uncooperative and surveillance scenarios. Accenture Multimodality 1 (ACC-MM1) is a large-scale multimodal biometric recognition dataset composed of imagery and video. The dataset includes challenges such as long ranges, high pitch angles, varied atmospheric conditions, and mixed image quality levels. Ultimately, a dataset containing 227 unique subjects, 303 hours of video, and 12,344 still images was captured in indoor and outdoor conditions. In addition to traditional modalities (face, gait, etc.), data for a novel biometric modality, activity gait, was collected. Covariates included appearance changes, walking with weighted loads, and body distortions. Furthermore, to enable standardized performance testing of ACC-MM1, an evaluation protocol was created. Baseline performance of popular and novel recognition algorithms is reported to encourage research in the challenging conditions present in ACC-MM1.

Related Material

@InProceedings{O'Brien_2024_WACV, author = {O'Brien, Kyle and Rybak, Michelle and Huang, Jiong and Stevens, Adam and Fredriksz, Madeline and Chaberski, Michael and Russell, Danielle and Castin, Lindsey and Jou, Michelle and Gurrapadi, Nishant and Bosch, Marc}, title = {Accenture-MM1: A Multimodal Person Recognition Dataset}, booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) Workshops}, month = {January}, year = {2024}, pages = {112-122} }