TY - GEN
T1 - A Study of Privacy Protection of Photos Taken by a Wide-angle Surveillance Camera
AU - Nakai, Koki
AU - Nakai, Koki
AU - Kuribayashi, Minoru
AU - Funabiki, Nobuo
N1 - Funding Information:
This research was supported by JSPS KAKENHI Grant Number 19K22846, JST SICORP Grant Number JP-MJSC20C3, and JST CREST Grant Number JPMJCR20D3, Japan. We would like to thank Editage (www.editage.com) for English language editing.
Publisher Copyright:
© 2021 APSIPA.
PY - 2021
Y1 - 2021
N2 - In this paper, we propose a privacy protection system that detects human faces from images captured by a wide-angle camera, which is assumed to be a surveillance camera, and encrypts the face detection area. In the proposed face detection method, a classifier is created by AdaBoost learning based on Haar-like features, and the face region is detected from the image captured by the wide-angle camera. By creating the training data based on the face images captured by the camera, faces without can be detected compromising the detection accuracy, even for surveillance cameras. We use block-scrambling encryption to protect the privacy of the detected face area. During face detection, minimizing the probability of missing a face and allowing a certain number of false positives are necessary from the privacy protection viewpoint. In the case of false positives in previous encryption methods, the color space of the background cannot be preserved, resulting in visual degradation. Therefore, in the proposed encryption method, visual degradation is suppressed by improving the processing of the color components. Through simulations, we evaluate the effectiveness of the proposed method in terms of detection accuracy and processing speed for face detection, as well as color component change and compression efficiency for encryption.
AB - In this paper, we propose a privacy protection system that detects human faces from images captured by a wide-angle camera, which is assumed to be a surveillance camera, and encrypts the face detection area. In the proposed face detection method, a classifier is created by AdaBoost learning based on Haar-like features, and the face region is detected from the image captured by the wide-angle camera. By creating the training data based on the face images captured by the camera, faces without can be detected compromising the detection accuracy, even for surveillance cameras. We use block-scrambling encryption to protect the privacy of the detected face area. During face detection, minimizing the probability of missing a face and allowing a certain number of false positives are necessary from the privacy protection viewpoint. In the case of false positives in previous encryption methods, the color space of the background cannot be preserved, resulting in visual degradation. Therefore, in the proposed encryption method, visual degradation is suppressed by improving the processing of the color components. Through simulations, we evaluate the effectiveness of the proposed method in terms of detection accuracy and processing speed for face detection, as well as color component change and compression efficiency for encryption.
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M3 - Conference contribution
AN - SCOPUS:85126667445
T3 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
SP - 1865
EP - 1871
BT - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2021
Y2 - 14 December 2021 through 17 December 2021
ER -