Biomobile: sistema de identificação de usuários em dispositivos móveis na plataforma Android utilizando reconhecimento de faces a partir de vídeo

Pós-graduação em Ciência da Computação - IBILCE

Caracterização do desempenho de escolares com e sem dificuldades de aprendizagem em tarefas de consciência fonológica

Learning to read and write at an early stage is the process of transferring the sound form of the spoken language for the graphical form of writing, a process, a time that in our system of alphabetical called writing, the letters are graphical representations in the level of phoneme. So that this representation occurs, it is necessary that the individual already can of some form perceive and manipulate the different sonorous segments of the word. This capacity of perception directed to the segments of the word calls Phonological Awareness. Thus, it was established had for objective to verify the pertaining to school performance of 1ª to 4ª series with and without of learning in Tests of Phonological Awareness. Fourth children with age average of 9 years and 3 months without learning disabilities had been submitted to the Protocol of Phonological Awareness (CIELO, 2002) using of this instrument had participated of this study 80 pertaining to school of both only the phonological tasks. The data received from quantiqualitative approach whose results were extracted inferences. The statistically significant results occurred in the tasks of Realism Face Detection, Syllables, Detecting Phonemes, Phonemic Synthesis and Reversal Phonemic. Based on the results we observed that children without learning difficulties performed better on all tasks mentioned above

Human face verification based on multidimensional Polynomial Powers of Sigmoid (PPS)

In this paper, we described how a multidimensional wavelet neural networks based on Polynomial Powers of Sigmoid (PPS) can be constructed, trained and applied in image processing tasks. In this sense, a novel and uniform framework for face verification is presented. The framework is based on a family of PPS wavelets,generated from linear combination of the sigmoid functions, and can be considered appearance based in that features are extracted from the face image. The feature vectors are then subjected to subspace projection of PPS-wavelet. The design of PPS-wavelet neural networks is also discussed, which is seldom reported in the literature. The Stirling Universitys face database were used to generate the results. Our method has achieved 92 % of correct detection and 5 % of false detection rate on the database.

Adaptative techniques for the human faces detection

This paper presents results from an efficient approach to an automatic detection and extraction of human faces from images with any color, texture or objects in background, that consist in find isosceles triangles formed by the eyes and mouth.

Intrusion detection in computer networks using optimum-path forest clustering

Nowadays, organizations face the problem of keeping their information protected, available and trustworthy. In this context, machine learning techniques have also been extensively applied to this task. Since manual labeling is very expensive, several works attempt to handle intrusion detection with traditional clustering algorithms. In this paper, we introduce a new pattern recognition technique called Optimum-Path Forest (OPF) clustering to this task. Experiments on three public datasets have showed that OPF classifier may be a suitable tool to detect intrusions on computer networks, since it outperformed some state-of-the-art unsupervised techniques. © 2012 IEEE.