Yoanna Martínez-Díaz

TL;DR: The proposed method not only encodes jointly the local spatial and temporal information, but also extracts the most discriminative facial dynamic information while trying to discard spatio-temporal features related to intra-personal variations.

TL;DR: This paper studies the impact of lightweight face models on real applications and evaluates the performance of five recent lightweight architectures on five face recognition scenarios: image and video based face recognition, cross-factor and heterogeneous face Recognition, as well as active authentication on mobile devices.

TL;DR: A new way for obtaining FV encoding of binary features that is still efficient and also accurate, and shows that BRIEF combined with FV are discriminative enough, and provide as good performance as the one obtained by using SIFT features for video face recognition.

TL;DR: This work proposes new relevant evaluation protocols for the YouTube Faces database (REP-YTF) supporting face verification and open/closed-set identification and provides an extensive experimental evaluation, by combining several well-established feature representations with three different metric learning algorithms.

TL;DR: In this paper, the authors conduct an ISO/IEC Standards 24745 and 30136 compliant assessment of block-based warping sample transformation techniques aiming for template protection, focusing on the results' evaluation considering the evolution of face recognition technology ranging from more “historic” hand-crafted features to state-of-the-art deep-learning (DL) based schemes.