(Retracted) Robust face recognition using multimodal data and transfer learning

Srivastava, Akhilesh Mohan; Chintaginjala, Sai Dinesh; Bhogavalli, Samhit Chowdary; Prakash, Surya

doi:10.1117/1.jei.32.4.042105

Cited by 2 publications

(1 citation statement)

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“…The ODU Vision Lab has ongoing work in gender classification and person classification using optical and infrared special purpose sensors [1][2][3][4][5]. Infrared sensors compliment the abilities of visual range sensors for human subject analysis including face recognition [6][7][8][9], action recognition [10][11][12][13][14], and gender [15][16][17][18][19][20] and identity [20][21][22][23][24][25][26] recognition. These sensors may be deployed in the field with a small amount of training data available to establish recognition models.…”

Section: Introductionmentioning

confidence: 99%

Transfer learning using infrared and optical full motion video data for gender classification

Glandon

Zalameda

Iftekharuddin

2023

Infrared Technology and Applications XLIX

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This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.

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Section: Introductionmentioning

confidence: 99%

Transfer learning using infrared and optical full motion video data for gender classification

Glandon

Zalameda

Iftekharuddin

2023

Infrared Technology and Applications XLIX

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Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

Al-Mannai,

Al-Thelaya,

Schneider

et al. 2024

IEEE Access

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Due to their ease-of-use, biometric verification methods to control access to digital devices have become ubiquitous. Many rely on supervised machine learning, a process that is notoriously datahungry. At the same time, biometric data is sensitive from a privacy perspective, and a comprehensive review from a data set perspective is lacking. In this survey, we present a comprehensive review of multimodal face data sets (e.g., data sets containing RGB color plus other channels such as infrared or depth). This follows a trend in both industry and academia to use such additional modalities to improve the robustness and reliability of the resulting biometric verification systems. Furthermore, such data sets open the path to a plethora of additional applications, such as 3D face reconstruction (e.g., to create avatars for VR and AR environments), face detection, registration, alignment, and recognition systems, emotion detection, anti-spoofing, etc. We also provide information regarding the data acquisition setup and data attributes (ethnicities, poses, facial expressions, age, population size, etc.) as well as a thorough discussion of related applications and state-of-the-art benchmarking. Readers may thus use this survey as a tool to navigate the existing data sets both from the application and data set perspective. To existing surveys we contribute, to the best of our knowledge, the first exhaustive review of multimodalities in these data sets.

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(Retracted) Robust face recognition using multimodal data and transfer learning

Cited by 2 publications

References 65 publications

Transfer learning using infrared and optical full motion video data for gender classification

Transfer learning using infrared and optical full motion video data for gender classification

Multimodal Face Data Sets—A Survey of Technologies, Applications, and Contents

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