This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.

The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.

This two-volume set, LNCS 15352 and LNCS 15353, constitutes the proceedings of the 18th Chinese Conference on Biometric Recognition, CCBR 2024, held in Nanjing, China, during November 22–24, 2024.

The 52 full papers presented here were carefully reviewed and selected from 82 submissions. These papers have been categorized under the following topical sections in these two volumes: -

Part I: Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking.

Part II: Face Detection, Recognition and Tracking; Affective Computing and Human-Computer Interface; Gait, Iris and Other Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.

This Springer Brief represents a comprehensive review of information theoretic methods for robust recognition. A variety of information theoretic methods have been proffered in the past decade, in a large variety of computer vision applications; this work brings them together, attempts to impart the theory, optimization and usage of information entropy.

The authors resort to a new information theoretic concept, correntropy, as a robust measure and apply it to solve robust face recognition and object recognition problems. For computational efficiency, the brief introduces the additive and multiplicative forms of half-quadratic optimization to efficiently minimize entropy problems and a two-stage sparse presentation framework for large scale recognition problems. It also describes the strengths and deficiencies of different robust measures in solving robust recognition problems.

This two-volume set, LNCS 15352 and LNCS 15353, constitutes the proceedings of the 18th Chinese Conference on Biometric Recognition, CCBR 2024, held in Nanjing, China, during November 22–24, 2024.

The 52 full papers presented here were carefully reviewed and selected from 82 submissions. These papers have been categorized under the following topical sections in these two volumes: -

Part I: Fingerprint, Palmprint and Vein Recognition; Face Detection, Recognition and Tracking.

Part II: Face Detection, Recognition and Tracking; Affective Computing and Human-Computer Interface; Gait, Iris and Other Biometrics; Trustworthy, Privacy and Personal Data Security; Medical and Other Applications.