Assessing the Influencing Factors on the Accuracy of Underage Facial Age Estimation
作者： Nhien-An Le-Khac , Brett A. Becker , Mark Scanlon , David Lillis , Felix Anda
DOI: 10.1109/CYBERSECURITY49315.2020.9138851
关键词: Biometrics 、 Business process discovery 、 Digital forensics 、 Law enforcement 、 Service (systems architecture) 、 Data science 、 Variance (accounting) 、 Cloud computing 、 Digital evidence 、 Computer science
摘要: Swift response to the detection of endangered minors is an ongoing concern for law enforcement. Many child-focused investigations hinge on digital evidence discovery and analysis. Automated age estimation techniques are needed aid in these expedite this process, decrease investigator exposure traumatic material. also show promise decreasing overflowing backlog obtained from increasing numbers devices online services. A lack sufficient training data combined with natural human variance has been long hindering accurate automated – especially underage subjects. This paper presented a comprehensive evaluation performance two cloud services (Amazon Web Service’s Rekognition service Microsoft Azure’s Face API) against dataset over 21,800 The objective work evaluate influence that certain biometric factors, facial expressions, image quality (i.e. blur, noise, resolution) have outcome thorough allows us identify most influential factors be overcome future systems.
forensicsandsecurity.com参考文章(21)
U.A. Khan, M.I. Cheema, N.M. Sheikh, Adaptive video encoding based on skin tone region detection IEEE Students Conference, ISCON '02. Proceedings.. ,vol. 1, pp. 129- 134 ,(2002) , 10.1109/ISCON.2002.1215952
Robert Moore, Cybercrime: Investigating High-Technology Computer Crime ,(2006)
Antitza Dantcheva, Cunjian Chen, Arun Ross, Impact of facial cosmetics on automatic gender and age estimation algorithms international conference on computer vision theory and applications. pp. 182- 190 ,(2014)
Antitza Dantcheva, Cunjian Chen, Arun Ross, Can facial cosmetics affect the matching accuracy of face recognition systems international conference on biometrics theory applications and systems. pp. 391- 398 ,(2012) , 10.1109/BTAS.2012.6374605
Xiaolong Wang, Vincent Ly, Guoyu Lu, Chandra Kambhamettu, None, Can We Minimize the Influence Due to Gender and Race in Age Estimation international conference on machine learning and applications. ,vol. 2, pp. 309- 314 ,(2013) , 10.1109/ICMLA.2013.141
Darren Quick, Kim-Kwang Raymond Choo, Impacts of increasing volume of digital forensic data Digital Investigation. ,vol. 11, pp. 273- 294 ,(2014) , 10.1016/J.DIIN.2014.09.002
Davis E. King, Dlib-ml: A Machine Learning Toolkit Journal of Machine Learning Research. ,vol. 10, pp. 1755- 1758 ,(2009) , 10.5555/1577069.1755843
Manuel C. Voelkle, Natalie C. Ebner, Ulman Lindenberger, Michaela Riediger, Let Me Guess How Old You Are: Effects of Age, Gender, and Facial Expression on Perceptions of Age Psychology and Aging. ,vol. 27, pp. 265- 277 ,(2012) , 10.1037/A0025065
A. Midori Albert, Karl Ricanek, Eric Patterson, A review of the literature on the aging adult skull and face: Implications for forensic science research and applications Forensic Science International. ,vol. 172, pp. 1- 9 ,(2007) , 10.1016/J.FORSCIINT.2007.03.015
Jan Hauke, Tomasz Kossowski, Comparison of Values of Pearson's and Spearman's Correlation Coefficients on the Same Sets of Data Quaestiones Geographicae. ,vol. 30, pp. 87- 93 ,(2011) , 10.2478/V10117-011-0021-1