A neural-based minutiae pair identification method for touch-less fingerprint images

Contact-based sensors are the traditional devices used to capture fingerprint images in commercial and homeland security applications. Contact-less systems achieve the fingerprint capture by vision systems avoiding that users touch any parts of the biometric device. Typically, the finger is placed in the working area of an optics system coupled with a CCD module. The captured light pattern on the finger is related to the real ridges and valleys of the user fingertip, but the obtained images present important differences from the traditional fingerprint images. These differences are related to multiple factors such as light, focus, blur, and the color of the skin. Unfortunately, the identity comparison methods designed for fingerprint images captured with touch-based sensors do not obtain sufficient accuracy when are directly applied to touch-less images. Recent works show that multiple views analysis and 3D reconstruction can enhance the final biometric accuracy of such systems. In this paper we propose a new method for the identification of the minutiae pairs between two views of the same finger, an important step in the 3D reconstruction of the fingerprint template. The method is divisible in the sequent tasks: first, an image preprocessing step is performed; second, a set of candidate minutiae pairs is selected in the two images, then a list of candidate pairs is created; last, a set of local features centered around the two minutiae is produced and processed by a classifier based on a trained neural network. The output of the system is the list of the minutiae pairs present in the input images. Experiments show that the method is feasible and accurate in different light conditions and setup configurations

Identificação biométrica de gado bovino a partir de imagens do focinho

A identificação de gado bovino tem sido um problema grave para a associação de criadores. O presente trabalho tem como objetivo implementar um método para identificação de bovinos através de imagens biométricas do seu focinho. Para tal são avaliadas quatro metodologias de identificação, tendo em conta vários parâmetros de avaliação tais como: taxa de acerto, necessidade de pré-processamento e facilidade de utilização, bem como a velocidade de execução. A primeira metodologia intitulada por “Identificação através de pontos de Landmark” consiste na deteção destes pontos e através destes efetuar a correspondência entre as imagens. A segunda metodologia designada por “Correspondência espetral e Reweighted random walk matching (RRWM) ”, consiste na utilização de matrizes de afinidade e, a partir delas, encontrar correspondências entre imagens. A terceira metodologia denominada por “Identificação através de pontos SURF”, que se baseia em identificar as características e em efetuar a correspondência entre as imagens utilizando o método SURF (Speeded Up Robust Features). O último método designa-se por “Identificação utilizando o diagrama de Voronoi e a triangulação de Delaunay” este baseia-se na deteção dos centróides das glândulas e na identificação de características semelhantes através da triangulação de Delaunay. O método que produziu melhores resultados de avaliação foi a técnica de identificação baseada nas características obtidas pelo SURF. No final deste trabalho, e através dos resultados obtidos foi possível concluir-se que a metodologia adotada foi bem-sucedida obtendo uma taxa de acerto de 100 %, tornando-se assim numa alternativa válida de identificação.The identification of cattle has been a serious problem for the association of breeders. This work aims to implement a method for identifying cattle through yours biometric muzzle images. To this are evaluated four methodologies of identification, into account various evaluation parameters such as hit rate, the need for pre-processing and ease of use ad execution speed. The first methodology entitled by "Identification through Landmark points" consists in detecting these points and using that to make correspondence between the images. The second method called "Correspondence spectral and Reweighted Random Walk Matching (RRWM)" consists in the utilization of affinity matrices and from them, find correspondences between images. The third method referred to as "Identification through SURF points”, which is based on identifying characteristics and make correspondence between the images using the SURF method (Speeded Up Robust Features). The last method is referred to as "Identification using the Voronoi diagram and Delaunay triangulation". This is based initially for detection of centroids of the glands and identifying similar characteristics by Delaunay Triangulation. The method that produced the best results of evalution was the identification technique based on the characteristics obtained by SURF. At the end of this work, and through the obtained results we conclude that the methodology adopted was successful getting a 100% hit rate, thus becoming a valid alternative of the identification