Head poses and grimaces: Challenges for automated face identification algorithms?

URBANOVÁ, Petra, Tomáš GOLDMANN, Dominik ČERNÝ and Martin DRAHANSKÝ. Head poses and grimaces: Challenges for automated face identification algorithms? SCIENCE & JUSTICE. ENGLAND: ELSEVIER SCI LTD, 2024, vol. 64, No 4, p. 421-442. ISSN 1355-0306. Available from: https://dx.doi.org/10.1016/j.scijus.2024.06.002.

Basic information

• Original name: Head poses and grimaces: Challenges for automated face identification algorithms?
• Authors: URBANOVÁ, Petra (203 Czech Republic, guarantor, belonging to the institution), Tomáš GOLDMANN (203 Czech Republic), Dominik ČERNÝ (203 Czech Republic, belonging to the institution) and Martin DRAHANSKÝ (203 Czech Republic, belonging to the institution).
• Edition: SCIENCE & JUSTICE, ENGLAND, ELSEVIER SCI LTD, 2024, 1355-0306.

Other information

• Original language: English
• Type of outcome: Article in a journal
• Field of Study: 10201 Computer sciences, information science, bioinformatics
• Country of publisher: United Kingdom of Great Britain and Northern Ireland
• Confidentiality degree: is not subject to a state or trade secret
• WWW: URL
• Impact factor: Impact factor: 1.900 in 2022
• Organization unit: Faculty of Science
• Doi: http://dx.doi.org/10.1016/j.scijus.2024.06.002
• UT WoS: 001262494100001
• Keywords in English: Forensic image identification; Automated algorithms; Head pose; Facial expressions
• Tags: rivok
• Tags: International impact, Reviewed

Abstract

In today’s biometric and commercial settings, state-of-the-art image processing relies solely on artificial intelligence and machine learning which provides a high level of accuracy. However, these principles are deeply rooted in abstract, complex “black-box systems”. When applied to forensic image identification, concerns about transparency and accountability emerge. This study explores the impact of two challenging factors in automated facial identification: facial expressions and head poses. The sample comprised 3D faces with nine prototype expressions, collected from 41 participants (13 males, 28 females) of European descent aged 19.96 to 50.89 years. Pre-processing involved converting 3D models to 2D color images (256 × 256 px). Probes included a set of 9 images per individual with head poses varying by 5° in both left-to-right (yaw) and up-and-down (pitch) directions for neutral expressions. A second set of 3,610 images per individual covered viewpoints in 5° increments from −45° to 45° for head movements and different facial expressions, forming the targets. Pair-wise comparisons using ArcFace, a state-of-the-art face identification algorithm yielded 54,615,690 dissimilarity scores. Results indicate that minor head deviations in probes have minimal impact. However, the performance diminished as targets deviated from the frontal position. Right-to-left movements were less influential than up and down, with downward pitch showing less impact than upward movements. The lowest accuracy was for upward pitch at 45°. Dissimilarity scores were consistently higher for males than for females across all studied factors. The performance particularly diverged in upward movements, starting at 15°. Among tested facial expressions, happiness and contempt performed best, while disgust exhibited the lowest AUC values.

Links

• MUNI/A/1597/2023, interní kód MU: Name: Rozvoj automatizace při zpracování obrazových dat ve forenzní a aplikované antropologii

Investor: Masaryk University, Automated Image Processing in Forensic and Applied Anthropology

• VB02000062, research and development project: Name: Detekce a identifikace osob v davu na základě letecké prospekce

Investor: Ministry of the Interior of the CR, Person Detection and Identification in Crowds by Aerial Footage