Explainer: Footprint identification
Footprint identification is the measurement of footprint features for recognizing the identity of a user. A footprint is a universal and easy way to capture a personal “identifier” which does not change much over time.
Footprint-based measurements constitutes one of many new possibilities to realize biometric authentication. It is an experimental technology that is currently under development at a number of universities and research institutes.
Footprint identification is projected to become a new emerging alternative to access control in wellness domains such as spas and thermal baths. It has also been recommended as a technology to identify new born babies at hospitals.
Since footprints are not intended to support large-scale high security applications, such as electronic banking or building access control, the storage of footprint features does not necessarily imply security threats. On the other hand, due to the practice of wearing shoes, it is difficult for impostors to obtain footprints for forgery attacks. Thus, footprint-based recognition could potentially be an alternative for high-security military applications.
Multiple variations of footprint identification are currently being developed by various research groups working worldwide. As this technology evolves, most versions are projected to use approaches comparable to state-of-the-art hand geometry, palm print and fingerprint techniques. Such a technology would examine friction ridge, texture and foot shape and even foot silhouette.
Current prototypes of footprint identification technology use cameras to capture naked footprints. Then the images undergo pre-processing, followed by the extraction of two features: shape using gradient vector flow (GVF), and minutiae extraction respectively. Matching is then effected based on these two features followed by a fusion of these two results for either a “reject” or “accept” decision. Shape matching features are typically based on cosine similarity while texture is based on miniature score matching.
A high recognition rate by verifying raw footprints directly is difficult to obtain, because people stand in various positions with different distances and angles between the two feet. To achieve robustness in matching an input pair of footprints with those of registered footprints, the input pair of footprints must be normalized in position and direction. Such normalization might remove useful information for recognition, so geometric information of the footprint should be included to ensure the standardization and normalization of capture image foot features.