User Authentication and Vital Signs Extraction from Low-Frame-Rate and Monochrome No-contact Fingerprint Captures
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arXiv
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| Format: | Preprint |
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2024
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| author | Olugbenle, Olaoluwayimika Drake, Logan Venkataswamy, Naveenkumar G. Rahman, Arfina Afolayanka, Yemi Imtiaz, Masudul Banavar, Mahesh K. |
| author_facet | Olugbenle, Olaoluwayimika Drake, Logan Venkataswamy, Naveenkumar G. Rahman, Arfina Afolayanka, Yemi Imtiaz, Masudul Banavar, Mahesh K. |
| contents | We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research predominantly utilizes high-frame-rate, multi-wavelength PPG sensors (e.g., infrared, red, or RGB), our preliminary findings demonstrate that both user identification and vital sign extraction are achievable with the low-frame-rate data we collected. Preliminary results are promising, with low error rates for both heart rate estimation and user authentication. These results indicate promise for effective biometric systems. We anticipate further optimization will enhance accuracy and advance healthcare and security. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | User Authentication and Vital Signs Extraction from Low-Frame-Rate and Monochrome No-contact Fingerprint Captures Olugbenle, Olaoluwayimika Drake, Logan Venkataswamy, Naveenkumar G. Rahman, Arfina Afolayanka, Yemi Imtiaz, Masudul Banavar, Mahesh K. Image and Video Processing Cryptography and Security Machine Learning We present our work on leveraging low-frame-rate monochrome (blue light) videos of fingertips, captured with an off-the-shelf fingerprint capture device, to extract vital signs and identify users. These videos utilize photoplethysmography (PPG), commonly used to measure vital signs like heart rate. While prior research predominantly utilizes high-frame-rate, multi-wavelength PPG sensors (e.g., infrared, red, or RGB), our preliminary findings demonstrate that both user identification and vital sign extraction are achievable with the low-frame-rate data we collected. Preliminary results are promising, with low error rates for both heart rate estimation and user authentication. These results indicate promise for effective biometric systems. We anticipate further optimization will enhance accuracy and advance healthcare and security. |
| title | User Authentication and Vital Signs Extraction from Low-Frame-Rate and Monochrome No-contact Fingerprint Captures |
| topic | Image and Video Processing Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2412.07082 |