OPTIMIZED BIOMETRIC SYSTEM BASED ON COMBINATION OF FACE IMAGES AND LOG TRANSFORMATION

The biometrics are used to identify a person effectively. In this paper, we propose optimised Face recognition system based on log transformation and combination of face image features vectors. The face images are preprocessed using Gaussian filter to enhance the quality of an image. The log transformation is applied on enhanced image to generate features. The feature vectors of many images of a single person image are converted into single vector using average arithmetic addition. The Euclidian distance(ED) is used to compare test image feature vector with database feature vectors to identify a person. It is experimented that, the performance of proposed algorithm is better compared to existing algorithms

A study on different experimental configurations for age, race, and gender estimation problems

This paper presents a detailed study about different algorithmic configurations for estimating soft biometric traits. In particular, a recently introduced common framework is the starting point of the study: it includes an initial facial detection, the subsequent facial traits description, the data reduction step, and the final classification step. The algorithmic configurations are featured by different descriptors and different strategies to build the training dataset and to scale the data in input to the classifier. Experimental proofs have been carried out on both publicly available datasets and image sequences specifically acquired in order to evaluate the performance even under real-world conditions, i.e., in the presence of scaling and rotation

What's in a smile? Initial analyses of dynamic changes in facial shape and appearance

Single-level principal component analysis (PCA) and multi-level PCA (mPCA) methods are applied here to a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Inspection of eigenvalues gives insight into the importance of different factors affecting shapes, including: biological sex, facial expression (neutral versus smiling), and all other variations. Biological sex and facial expression are shown to be reflected in those components at appropriate levels of the mPCA model. Dynamic 3D shape data for all phases of a smile made up a second dataset sampled from 60 adult British subjects (31 male; 29 female). Modes of variation reflected the act of smiling at the correct level of the mPCA model. Seven phases of the dynamic smiles are identified: rest pre-smile, onset 1 (acceleration), onset 2 (deceleration), apex, offset 1 (acceleration), offset 2 (deceleration), and rest post-smile. A clear cycle is observed in standardized scores at an appropriate level for mPCA and in single-level PCA. mPCA can be used to study static shapes and images, as well as dynamic changes in shape. It gave us much insight into the question “what’s in a smile?