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Soft Computing for Image Processing in Biometric Recognition Systems

Mar

02

2025

Soft Computing for Image Processing in Biometric Recognition Systems

Scope of the Special Issue:

The need for high-security systems is driving up the importance of biometrics, or the computer-based verification of an individual's identification. A biometric system verifies the validity of a particular physiological or behavioral trait that an individual possesses. The employment of new computer technology is required to meet higher expectations over actual biometric systems, such as resilience, higher identification rates, endurance for uncertainties and ambiguity, and adaptability. Soft computing is thus being utilized more and more in the creation of biometric applications. Biometric systems based on image processing are frequently employed in applications that require recognition, safety identification, and expedited login procedures. Due to the increasingly individualized quantity of information available in these databases, the privacy problems connected with soft biometric-assisted designs significantly grow.

As computer-based systems hold a growing variety of sensitive data, biometric-based verification is also becoming more and more crucial. Resilience, high identification rates, the ability to manage imprecision, uncertainty of a non-statistical nature, and generous flexibility are the new requirements placed on biometric systems. The application of soft computing techniques is particularly relevant in this situation. This article's primary goal is to analyze the implications of using soft computing approaches to biometrics from a pragmatic point of perspective. It has been discovered that soft computing has already gained traction, either alone or in conjunction with other techniques. Fuzzy logic and evolutionary computation algorithms are ranked second and third, respectively, after applications of various neural network types. The physiological or behavioral traits of an individual are measured using biometrics. It makes it possible to use these metrics for identity authentication. This means that since computer-based apps hold a growing variety of confidential information, biometric-based authentication is becoming more and more crucial. Given the restricted funding of integrated computer systems, biometric-based encryption is particularly challenging to deploy in these applications. So, to further enhance performance, soft-biometric features might be taken into account in addition to hard-biometric characteristics. Enhancing verification performance further can be achieved by combining soft computing techniques and soft biometric data, as they reduce processor and memory needs.

This special issue presents an overview of biometric soft computing techniques. Although biometrics has emerged as one of the most potential methods of verification, problems with false recognition and denial rates still exist. Soft computing has become increasingly popular recently, especially in the field of biometric recognition, where it has significantly increased recognition rates. An expanding number of soft computing approaches, such as fuzzy logic, evolutionary computation, and artificially generated neural networks, are being employed to build effective biometric systems.We accept submissions from a variety of fields and viewpoints, such as but not limited to: Soft Computing for Image Processing in Biometric Recognition Systems.

Potential list of topics includes:

Smarter Systems: Progress in Soft Computing, Biometric Systems, and Image Processing
Image processing and pattern recognition using a soft computing technique
Soft computing and neural network developments in image processing
Application of soft computing approaches for multimodal biometric authentication technology
A Systematic Soft Computing Method for a biometric Safety Architecture
Utilising soft computing techniques for multimodal biometric authorization system
Combining fingerprint and iris data with fuzzy logic for multimodal biometric system
Protection of privacy in a multimodal detection system based on soft biometrics
Multimodal exploration of the retinal fundus using soft computing algorithms
Soft biometrics powered by machine learning for improved keystroke identification
Intelligent system evaluation and development utilising soft computing methods