Behavioral Biometrics – Gait Analysis
Biometrics are the characteristics that can be used to identify an individual. There are two main types of biometrics; behavioral biometrics and physical biometrics. On the one hand, physical biometrics identify an individual’s physical characteristics such as fingerprints and retina scans, particular to one individual. On the other hand, behavioral biometrics identify an individual’s characteristics that are based on their behavior, such as writing rhythm, typing rhythm and speed (keystroke dynamics), voice recognition, and walking patterns (gait biometric) (Revett, 2009). Accordingly, these behavioral biometrics have diverse applications, including access control and criminal investigations; one such significant behavioral biometric is gait analysis.
Gait is the design of locomotion or walking patterns (Revett, 2009); gait analysis is thus an analysis of walking patterns. People have different manners of ambulation, creating slight differences in gait. According to Revett (2009), there are two aspects of gait analysis biometrics; sensor-based and machine-vision-based analysis. These two strategies of gait analysis biometrics work differently. In consideration of the number of people, machine vision analysis is used with a large mass of people, and analysis can be conducted over a distance without direct contact with the individual being analyzed. Additionally, data acquisition must be swift for this approach to work because the individuals being analyzed are continuously moving further away.
The second approach to gait analysis is sensor-based analysis. The sensor-based analysis is used per individual since the sensor is attached to the individual undergoing the analysis (Revett, 2009). Accordingly, this approach is not limited to speed since the device can be disconnected from the individual at any given time. Therefore, for this reason, the sensor-based analysis approach is primarily applied for clinical purposes, especially during locomotive therapies. Consistently, during either of these gait analysis techniques, various factors are established, including joint kinetics and kinematics, foot pressure, and oxygen consumption, amongst others, to yield the analysis data.
Accordingly, due to the characteristics of gait analysis, it can be applied during criminal investigations. With technological advancements, camera surveillance has become a part of people’s lives with their usage over numerous locations such as supermarkets and streets. Consistently, surveillance over all these areas yields recordings that can capture perpetrators of criminal activities, making gait analysis important in criminal investigations. During criminal investigations, gait analysis is conducted by forensic investigators and therefore falls under forensic evidence. In most cases, forensic gait analysis is used as a supportive piece of evidence since a perpetrator of a crime is not identified using the analysis, but their presence will be identified in the gait analysis.
Gait analysis can be used in criminal investigations using the machine-vision-based gait analysis approach. Its use is based on the videos collected from camera surveillance, and currently, with technological advancement, automated gait recognition has been developed. Moreover, automated gait recognition provides data that new data obtained can be compared to during criminal investigations (van Mastrigt et al., 2018). Subsequently, camera surveillance collected near crime scenes can be compared using automated gait recognition to reduce the number of suspects by narrowing down on a crime perpetrator. Further, with its use as supportive pieces of evidence, gait analysis has been used in courts in countries such as the Netherlands and the United Kingdom.
Despite the advantages posed to criminal investigations, gait analysis has several limitations. Firstly, the clarity of the videos collected for the analysis limits the identification of suspects (van Mastrigt et al., 2018). Accordingly, its reliability as evidence is questioned in certain circumstances. Secondly, the accuracy of gait analysis is also contested with limitations from factors such as fatigue, location, footwear, and lighting (Revett, 2009). Additionally, other factors may also affect the walking pattern resulting in gait analysis limitations.
In conclusion, behavioral biometrics are significant in everyday life with their diverse applications. One significant application is in criminal investigations whereby a perpetrator can be identified amongst suspects based on their characteristics. Accordingly, biometrics are essential in the world today, especially in criminal investigations.
References
Revett, K. (2009). Behavioral Biometrics. John Wiley & Sons, Ltd.
van Mastrigt, N., Celie, K., Mieremet, A., Ruifrok, A., & Geradts, Z. (2018). Critical review of the use and scientific basis of forensic gait analysis. Forensic Sciences Research, 3(3), 183-193. https://doi.org/10.1080/20961790.2018.1503579
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Question 
Behavioral Biometrics – Gait Analysis
Behavioral biometrics are based on a person’s actions or measurements of body movement. Respond to the following:
• Select from one of the following lower or newer behavioral metrics:
o Facial recognition
o Signature analysis
o Gait analysis
o Keystroke analysis
• Explain how your selected biometric works.
• What are the ways that it be used in criminal investigations?
• Explain the limitations of your selected biometric.