Forensic Face Matching: Research and Practice

Face matching is a critical task for security purposes, but it is prone to error. Psychology has studied it extensively since 2010, providing insights into its application, efficacy, and limitations.

Format: Paperback / softback
Length: 272 pages
Publication date: 07 January 2021
Publisher: Oxford University Press

In our daily lives, we often encounter the task of recognizing faces, a seemingly straightforward and accurate endeavor. However, when it comes to unfamiliar faces, those we have never met before, we struggle to identify them, despite the critical importance of this ability in various everyday tasks. Forensic face matching is a crucial process that involves comparing two face photographs of a person who is unknown to the observer. While it may seem simple, this task holds immense significance for a wide range of security purposes, including person identification at airports and borders, passport issuance and renewal, and criminal identification in police investigations. Despite its widespread use, face matching is remarkably error-prone, even under optimal conditions. This has led to extensive research in the field of Psychology, with a significant surge in studies since 2010.

Forensic face matching offers a comprehensive and critical examination of facial comparison and face matching, shedding light on their applications, effectiveness, and limitations in occupational settings. It also provides an up-to-date overview of the current scientific knowledge of this task.

One of the key challenges in face matching is the variability in facial features, such as shape, size, and expression. Even slight differences in these features can make it difficult for humans to accurately identify faces. This variability is further exacerbated by factors such as lighting, facial expression, and age, which can further distort facial features.

To address these challenges, forensic face matching techniques have been developed. These techniques involve various algorithms and machine learning models that analyze the facial features of the photographs and compare them to identify similarities and differences. Some common techniques include template matching, which compares a face photograph to a set of pre-defined templates, and local feature matching, which analyzes specific regions of the face.

Despite the advancements in face matching technology, there are still limitations and potential errors. One of the major concerns is the potential for bias and discrimination in facial recognition systems. Studies have shown that facial recognition systems are more likely to misidentify individuals from marginalized communities, such as Black and Asian individuals, compared to white individuals. This bias can have serious consequences, particularly in law enforcement and security settings.

Another limitation of face matching is the potential for spoofing, where individuals create fake facial images or use digital manipulation techniques to evade facial recognition systems. Spoofing techniques can be highly sophisticated and difficult to detect, making it challenging to ensure the accuracy and reliability of facial recognition systems.

To address these limitations, researchers and practitioners in the field of forensic face matching are continuously working to improve the accuracy and reliability of facial recognition systems. This includes developing new algorithms and machine learning models that can better handle facial variability and reduce bias. It also involves implementing robust verification processes, such as facial recognition systems that can compare multiple photographs of a person to verify their identity.

In conclusion, forensic face matching is a critical task that plays a vital role in security and law enforcement. While it may seem straightforward, the challenges posed by facial variability, bias, and spoofing require careful consideration and development of advanced techniques. By continuing to invest in research and innovation, we can improve the accuracy and reliability of facial recognition systems and ensure that they are used in a fair and equitable manner.

Weight: 454g
Dimension: 171 x 245 x 21 (mm)
ISBN-13: 9780198837749