A Biometric Fusion Based on Face and Fingerprint Recognition using ANN

Biometric systems are used for identifying and recognizing individual characteristics on the basis of biological or behavioral features. In the research work, a biometric fusion system based on fingerprint and face using the artificial intelligence technique is proposed. To achieve better accuracy of the biometric fusion system, the uniqueness of feature is significant. To find out the unique feature set from the data, we have used different feature extraction algorithm in the proposed biometric fusion system. Initially, pre-processing has been applied on the test images which is used to remove the unwanted data from the uploaded image and return an appropriate data for further process. In the fingerprint part, minutia extraction is used as a feature of fingerprint whereas Extended Local Binary pattern (ELBP) is used for extracting features of face and creates a pattern of face features. To create a unique feature set, optimization algorithm is needed and we have used genetic algorithm as a feature optimization technique. In the proposed fusion system, ANN is used to classify the test data according to the trained ANN structure with optimized feature data of fingerprint and face. To check the efficiency of proposed fusion system, we have calculated the performance parameters like FAR, FRR and Accuracy. From the analysis of proposed fusion system, we have observed that the accuracy of the proposed work is better than the previous ones and it is more than the 94%. To design a proposed biometric fusion system, image processing toolbox is used under the MATLAB environment

Facial Paralysis Grading Based on Dynamic and Static Features

Peripheral facial nerve palsy, also known as facial paralysis (FP), is a common clinical disease, which requires subjective judgment and scoring based on the FP scale. There exists some automatic facial paralysis grading methods, but the current methods mostly only consider either static or dynamic features, resulting in a low accuracy rate of FP grading. This thesis proposes an automatic facial paralysis assessment method including both static and dynamic characteristics. The first step of the method performs preprocessing on the collected facial expression videos of the subjects, including rough video interception, video stabilization, keyframe extraction, image geometric normalization and gray-scale normalization. Next, the method selects as keyframes no facial expression state and maximum facial expression state in the image data to build the the research data set. Data preprocessing reduces errors, noise, redundancy and even errors in the original data. The basis for extracting static and dynamic features of an image is to use Ensemble of Regression Trees algorithm to determine 68 facial landmarks. Based on landmark points, image regions of image are formed. According to the Horn-Schunck optical flow method, the optical flow information of parts of the face are extracted, and the dynamic characteristics of the optical flow difference between the left and right parts are calculated. Finally, the results of dynamic and static feature classification are weighted and analyzed to obtain FP ratings of subjects. A 32-dimensional static feature is fed into the support vector machine for classification. A 60-dimensional feature vector of dynamical aspects is fed into a long and short-term memory network for classification. Videos of 30 subjects are used to extract 1419 keyframes to test the algorithm. The accuracy, precision, recall and f1 of the best classifier reach 93.33%, 94.29%, 91.33% and 91.87%, respectively.Perifeerinen kasvojen hermohalvaus, joka tunnetaan myös nimellä kasvojen halvaus (FP), on yleinen kliininen sairaus, joka vaatii subjektiivista arviointia ja FP -asteikon pisteytystä. Joitakin automaattisia kasvohalvauksen luokittelumenetelmiä on olemassa, mutta yleensä ottaen ne punnitsevat vain joko staattisia tai dynaamisia piirteitä. Tässä tutkielmassa ehdotetaan automaattista kasvojen halvaantumisen arviointimenetelmää, joka kattaa sekä staattiset että dynaamiset ominaisuudet. Menetelmän ensimmäinen vaihe suorittaa ensin esikäsittelyn kohteiden kerätyille kasvojen ilmevideoille, mukaan lukien karkea videon sieppaus, videon vakautus, avainruudun poiminta, kuvan geometrinen normalisointi ja harmaasävyjen normalisointi. Seuraavaksi menetelmä valitsee avainruuduiksi ilmeettömän tilan ja kasvojen ilmeiden maksimitilan kuvadatasta kerryttäen tutkimuksen data-aineiston. Tietojen esikäsittely vähentää virheitä, kohinaa, redundanssia ja jopa virheitä alkuperäisestä datasta. Kuvan staattisten ja dynaamisten piirteiden poimimisen perusta on käyttää Ensemble of Regression Trees -algoritmia 68 kasvojen merkkipisteiden määrittämiseen. Merkkipisteiden perusteella määritellään kuvan kiinnostavat alueet. Horn-Schunckin optisen virtausmenetelmän mukaisesti poimitaan optisen virtauksen tiedot joistakin kasvojen osista, ja dynaaminen luonnehdinta lasketaan vasempien ja oikeiden osien välille. Lopuksi dynaamisen ja staattisen piirteiden luokittelun tulokset painotetaan ja analysoidaan kattavasti koehenkilöiden FP-luokitusten saamiseksi. 32- ulotteinen staattisten piirteiden vektori syötetään tukivektorikoneeseen luokittelua varten. 60-ulotteinen dynaamisten piirteiden ominaisuusvektori syötetään pitkän ja lyhyen aikavälin muistiverkkoon luokittelua varten. Parhaan luokittelijan tarkkuus, täsmällisyys, palautustaso ja f1 saavuttavat arvot 93,33%, 94,29%, 91,33% ja 91,87%

Finger Vein Recognition Using Principle Component Analysis and Adaptive k-Nearest Centroid Neighbor Classifier

The k-nearest centroid neighbor kNCN classifier is one of the non-parametric classifiers which provide a powerful decision based on the geometrical surrounding neighborhood. Essentially, the main challenge in the kNCN is due to slow classification time that utilizing all training samples to find each nearest centroid neighbor. In this work, an adaptive k-nearest centroid neighbor (akNCN) is proposed as an improvement to the kNCN classifier. Two new rules are introduced to adaptively select the neighborhood size of the test sample. The neighborhood size for the test sample is changed through the following ways: 1) The neighborhood size, k will be adapted to j if the centroid distance of j-th nearest centroid neighbor is greater than the predefined boundary. 2) There is no need to look for further nearest centroid neighbors if the maximum number of samples of the same class is found among jth nearest centroid neighbor. Thus, the size of neighborhood is adaptively changed to j. Experimental results on theFinger Vein USM (FV-USM) image database demonstrate the promising results in which the classification time of the akNCN classifier is significantly reduced to 51.56% in comparison to the closest competitors, kNCN and limited-kNCN. It also outperforms its competitors by achieving the best reduction ratio of 12.92% whilemaintaining the classification accuracy

Technique for recognizing faces using a hybrid of moments and a local binary pattern histogram

The face recognition process is widely studied, and the researchers made great achievements, but there are still many challenges facing the applications of face detection and recognition systems. This research contributes to overcoming some of those challenges and reducing the gap in the previous systems for identifying and recognizing faces of individuals in images. The research deals with increasing the precision of recognition using a hybrid method of moments and local binary patterns (LBP). The moment technique computed several critical parameters. Those parameters were used as descriptors and classifiers to recognize faces in images. The LBP technique has three phases: representation of a face, feature extraction, and classification. The face in the image was subdivided into variable-size blocks to compute their histograms and discover their features. Fidelity criteria were used to estimate and evaluate the findings. The proposed technique used the standard Olivetti Research Laboratory dataset in the proposed system training and recognition phases. The research experiments showed that adopting a hybrid technique (moments and LBP) recognized the faces in images and provide a suitable representation for identifying those faces. The proposed technique increases accuracy, robustness, and efficiency. The results show enhancement in recognition precision by 3% to reach 98.78%

Robust Image Analysis by L1-Norm Semi-supervised Learning

This paper presents a novel L1-norm semi-supervised learning algorithm for robust image analysis by giving new L1-norm formulation of Laplacian regularization which is the key step of graph-based semi-supervised learning. Since our L1-norm Laplacian regularization is defined directly over the eigenvectors of the normalized Laplacian matrix, we successfully formulate semi-supervised learning as an L1-norm linear reconstruction problem which can be effectively solved with sparse coding. By working with only a small subset of eigenvectors, we further develop a fast sparse coding algorithm for our L1-norm semi-supervised learning. Due to the sparsity induced by sparse coding, the proposed algorithm can deal with the noise in the data to some extent and thus has important applications to robust image analysis, such as noise-robust image classification and noise reduction for visual and textual bag-of-words (BOW) models. In particular, this paper is the first attempt to obtain robust image representation by sparse co-refinement of visual and textual BOW models. The experimental results have shown the promising performance of the proposed algorithm.Comment: This is an extension of our long paper in ACM MM 201

A Neural Model for Self Organizing Feature Detectors and Classifiers in a Network Hierarchy

Many models of early cortical processing have shown how local learning rules can produce efficient, sparse-distributed codes in which nodes have responses that are statistically independent and low probability. However, it is not known how to develop a useful hierarchical representation, containing sparse-distributed codes at each level of the hierarchy, that incorporates predictive feedback from the environment. We take a step in that direction by proposing a biologically plausible neural network model that develops receptive fields, and learns to make class predictions, with or without the help of environmental feedback. The model is a new type of predictive adaptive resonance theory network called Receptive Field ARTMAP, or RAM. RAM self organizes internal category nodes that are tuned to activity distributions in topographic input maps. Each receptive field is composed of multiple weight fields that are adapted via local, on-line learning, to form smooth receptive ftelds that reflect; the statistics of the activity distributions in the input maps. When RAM generates incorrect predictions, its vigilance is raised, amplifying subtractive inhibition and sharpening receptive fields until the error is corrected. Evaluation on several classification benchmarks shows that RAM outperforms a related (but neurally implausible) model called Gaussian ARTMAP, as well as several standard neural network and statistical classifters. A topographic version of RAM is proposed, which is capable of self organizing hierarchical representations. Topographic RAM is a model for receptive field development at any level of the cortical hierarchy, and provides explanations for a variety of perceptual learning data.Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409