Face Verification Based on Relational Disparity Features and Partial Least Squares Models

Vareto, Rafael Henrique; Silva, Samira; Costa, Filipe; Schwartz, William Robson

doi:10.1109/sibgrapi.2017.34

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…However, CNNs only became the most used feature extraction and learning techniques within the last decade. Combined with GPU and large datasets, face recognition methods are capable of surpassing human performance using deep network architectures, achieving state-of-the-art performance on many surveillance and biometrics tasks [10]- [15].…”

Section: Related Workmentioning

confidence: 99%

Open-set Face Recognition for Small Galleries Using Siamese Networks

Salomon

Britto

Vareto

et al. 2020

2020 International Conference on Systems, Signals and Image Processing (IWSSIP)

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Face recognition has been one of the most relevant and explored fields of Biometrics. In real-world applications, face recognition methods usually must deal with scenarios where not all probe individuals were seen during the training phase (openset scenarios). Therefore, open-set face recognition is a subject of increasing interest as it deals with identifying individuals in a space where not all faces are known in advance. This is useful in several applications, such as access authentication, on which only a few individuals that have been previously enrolled in a gallery are allowed. The present work introduces a novel approach towards open-set face recognition focusing on small galleries and in enrollment detection, not identity retrieval. A Siamese Network architecture is proposed to learn a model to detect if a face probe is enrolled in the gallery based on a verification-like approach. Promising results were achieved for small galleries on experiments carried out on Pubfig83, FRGCv1 and LFW datasets. State-ofthe-art methods like HFCN and HPLS were outperformed on FRGCv1. Besides, a new evaluation protocol is introduced for experiments in small galleries on LFW.

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Section: Related Workmentioning

confidence: 99%

Open-set Face Recognition for Small Galleries Using Siamese Networks

Salomon

Britto

Vareto

et al. 2020

2020 International Conference on Systems, Signals and Image Processing (IWSSIP)

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“…Several applications can be designed, including access permission to restricted places or bank accounts, for instance. Several works in the literature address face verification [3]- [6].…”

Section: Introductionmentioning

confidence: 99%

Self-portrait to ID Document face matching: CNN-Based face verification in cross-domain scenario

Costa

Melo

Gadelha

et al. 2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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Face verification approaches determine whether two given faces are from the same person. Recently, a new demand for face verification application which has become popular in commercial applications is the self-portrait and ID face matching, in which we compare the faces of a selfie shot by a subject and the face in a picture of her identification document. In this work, we proposed a novel approach for face verification in a cross-domain scenario, assuming we have only two images for each subject in the dataset. The method is based on siamese architecture with triplet-loss function. Experiments show the proposed model reaches good effectiveness for cross-domain face verification with low error rates, in comparison to other works of the literature.

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