Evolutionary facial feature selection

With the growing number of acquired physiological and behavioral biometric samples, biometric data sets are experiencing tremendous growth. As database sizes increase, exhaustive identification searches by matching with entire biometric feature sets become computationally unmanageable. An evolutionary facial feature selector chooses a set of features from prior contextual or meta face features that reduces the search space. This paper discusses and shows the results of an evolutionary computing approach with agglomerative k-means cluster spaces as input parameters into a LDA evaluation function to select facial features from the Carnegie Mellon University Pose, Illumination, and Expression database of human faces (PIE). Categories and Subject Descriptors I.5.3 [Pattern Recognition]: Clustering – algorithms. General Terms Algorithms, Performance, Experimentation, Theory. Keywords Application, Artificial Intelligence, Genetic algorithms, Pattern recognition and classification, Search....

• Type: Conference
• Year: 2008
• Where: GECCO
• Authors: Aaron K. Baughman
• Keywords: Biometric Data Sets, Biometric Feature Sets, Facial Features, GECCO 2008, Optimization