Imaging for Forensics and Security: From Theory to Practice by Ahmed Bouridane

By Ahmed Bouridane

Imaging for Forensics and safety: From concept to perform presents a close research of latest imaging and development reputation concepts for the knowledge and deployment of biometrics and forensic thoughts. those strategies can be utilized for sensible recommendations to extend safeguard. the fabric includes a choice of the new advances within the expertise starting from conception, layout, and implementation to functionality evaluate of biometric and forensic platforms. It additionally addresses new tools comparable to the multiscale technique, directional filter out financial institution, and wavelet maxima for the improvement of useful strategies to biometric problems.

The writer introduces a brand new forensic approach in accordance with shoeprint imagery with complex thoughts to be used in forensics functions. the fabric additionally offers the idea that of shielding the originality of biometric photos kept in databases opposed to intentional and unintended assaults and fraud detection information that allows you to additional raise the security.

Imaging for Forensics and safety: From thought to perform is an invaluable instrument for researchers and practitioners in imaging for defense and forensics.

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Additional info for Imaging for Forensics and Security: From Theory to Practice (Signals and Communication Technology)

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G. 1 Some commonly used biometrics Physical Behavioural Fingerprint: Most commonly used Higher false accept rate Gait: Face: Voice: Easiest to acquire Difficult to compar Hand Geometry: Robust under different conditions Changes occur with age Useful in distant Surveillance Changes with age and surface Useful in absence of visual data Changes with age and Health Handwriting & Signature: Useful in detecting emotions as well Changes with age, health and stress Palm Print: Bigger area of interest Availability of data set Iris: Very Low false accept rate Difficult to acquire Ear: Robust to change Difficult to acquire focuses on access control to critical area or application cost may not necessitate a significant consideration but uniqueness and circumvention may be important.

The system then performs a one-to-one match that compares a query face image against the template face image stored in the database whose identity is being claimed. If a match is made the identity of the person is verified. In other words, the verification test is conducted by dividing the subjects into two groups: • Clients, people trying to gain access using their own identity. e. an identity known to the system but not belonging to them. The percentage of imposters gaining access and clients rejected access are referred to as the False Acceptance Rate (FAR) and False Rejection Rate (FRR) for a given threshold, respectively.

1 Introduction Face recognition is one of the most popular applications in image processing and pattern recognition. It plays a very important role in many applications such as card identification, access control, mug shot searching, security monitoring and surveillance problems. There are several problems that make automatic face recognition a very challenging task. The input of a person’s face to a recognition system is usually acquired under different conditions from those of the corresponding image in the database.

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