Different Facial Recognition Techniques in Transform Domains

The human face is frequently used as the biometric signal presented to a machine for identification purposes. Several challenges are encountered while designing face identification systems. The challenges are either caused by the process of capturing the face image itself, or occur while processing the face poses. Since the face image not only contains the face, this adds to the data dimensionality, and thus degrades the performance of the recognition system. Face Recognition (FR) has been a major signal processing topic of interest in the last few decades. Most common applications of the FR include, forensics, access authorization to facilities, or simply unlocking of a smart phone. The three factors governing the performance of a FR system are: the storage requirements, the computational complexity, and the recognition accuracy. The typical FR system consists of the following main modules in each of the Training and Testing phases: Preprocessing, Feature Extraction, and Classification. The ORL, YALE, FERET, FEI, Cropped AR, and Georgia Tech datasets are used to evaluate the performance of the proposed systems. The proposed systems are categorized into Single-Transform and Two-Transform systems. In the first category, the features are extracted from a single domain, that of the Two-Dimensional Discrete Cosine Transform (2D DCT). In the latter category, the Two-Dimensional Discrete Wavelet Transform (2D DWT) coefficients are combined with those of the 2D DCT to form one feature vector. The feature vectors are either used directly or further processed to obtain the persons\u27 final models. The Principle Component Analysis (PCA), the Sparse Representation, Vector Quantization (VQ) are employed as a second step in the Feature Extraction Module. Additionally, a technique is proposed in which the feature vector is composed of appropriately selected 2D DCT and 2D DWT coefficients based on a residual minimization algorithm

Employing Vector Quantization On Detected Facial Parts For Face Recognition

Facial Parts Detection (FPD) approach in conjunction with Vector Quantization (VQ) algorithm are proposed in this paper for face recognition. There are three phases in the proposed system, namely, Preprocessing, Feature Extraction, and Classification. Detecting facial parts, which are nose, both eyes, and mouth, and choosing appropriate dimensions for each part are done in the preprocessing phase. In the feature extraction phase, four groups for each person, one group for each detected part, are constructed for dimensionality reduction and feature discrimination by considering all parts of all training poses. For further data compaction, VQ algorithm employing Kekre Fast Codebook Generation (KFCG) approach for codebook initialization is applied to each of the four groups. Finally, Euclidean distance criterion is used to obtain the recognition rates. Four databases, namely, ORL, YALE, FERET, and FEI that have different facial variations, such as illuminations, rotations, makeups, facial expressions, etc. are used to evaluate the proposed system. Experimental work is performed to evaluate the performance of the proposed technique and the state-of-the-arts approaches. Then, K-Fold Cross Validation (CV) is used to analyze the results. The proposed system consistently improved the recognition rates as well as the storage requirements. Sample results are given

Employing Vector Quantization Algorithm In A Transform Domain For Facial Recognition

A Vector Quantization (VQ) algorithm in the Discrete Cosine Transform (DCT) domain is proposed for facial recognition. There are three main phases in the proposed system, namely, Preprocessing, Feature Extraction, and Recognition. Cropping and choosing an appropriate dimension are performed in the preprocessing step. Then, DCT with appropriate truncation dimensions is applied to the processed faces for dimensionality re- duction. For further feature compaction, VQ algorithm employing Kekre Fast Codebook Generation (KFCG) approach for codebook initialization is applied to the transformed truncated features. Finally, the proposed system is extensively evaluated using four different databases, namely, ORL, YALE, FERET, and FEI that have different facial variations, such as illuminations, rotations, facial expressions, etc. Euclidean distance criterion is used to calculate the recognition rates. Then, the results are analyzed using K-fold Cross Validation (CV). The proposed approach is shown to improve the recognition rates as well as the storage requirements in comparison with some of the existing state-of-The arts approaches