Multi-feature Fusion Menggunakan Fitur Scale Invariant Feature Transform dan Local Extensive Binary Pattern untuk Pengenalan Pembuluh Darah pada Jari

Pengenalan pembuluh darah jari merupakan salah satu area dalam bidang biometrika. Sehingga tahap-tahap dalam proses pengenalan pembuluh darah jari memiliki kesamaan dengan proses pengenalan menggunakan biometrika lain yaitu meliputi pengumpulan citra, praproses, ekstraksi fitur, dan pencocokan. Tingkat keberhasilan dari tahap pencocokan ditentukan oleh pemilihan fitur pembuluh darah jari yang digunakan. Kondisi citra pembuluh darah yang rentan terhadap perubahan skala, rotasi maupun translasi menyebabkan kebutuhan akan fitur yang tahan terhadap kondisi tersebut menjadi hal yang penting. Fitur Scale Invariant Feature Transform (SIFT) adalah fitur yang telah cukup banyak digunakan untuk kasus pencocokan citra serta mampu tahan terhadap degradasi kondisi citra akibat perubahan skala, rotasi maupun translasi. Akan tetapi, fitur SIFT kurang memberikan hasil optimal jika diekstraksi dari citra dengan variasi tingkat keabuan seperti yang disebabkan oleh perbedaan intensitas pencahayaan. Fitur Local Extensive Binary Pattern (LEBP) merupakan fitur yang tahan terhadap variasi tingkat keabuan dengan informasi karakteristik lokal yang lebih kaya dan diskriminatif. Oleh karena itu digunakan teknik fusi untuk memperoleh informasi dari fitur SIFT dan fitur LEBP sehingga diperoleh fitur yang memiliki ketahanan terhadap degradasi kondisi citra akibat perubahan skala, rotasi, translasi, variasi tingkat keabuan seperti yang disebabkan oleh perbedaan intensitas pencahayaan. Penelitian ini mengusulkan multi-feature fusion menggunakan fitur SIFT dan LEBP untuk pengenalan pembuluh darah pada jari. Fitur hasil fusion diproses dengan metode Learning Vector Quantization (LVQ) untuk menentukan apakah citra pembuluh darah jari yang diuji dapat dikenali atau tidak. Dengan menggunakan multi-feature fusion diharapkan mampu representasi fitur yang dapat meningkatkan akurasi dari proses pengenalan pembuluh darah jari meskipun fitur diambil dari citra yang mengalami degradasi. Berdasarkan hasil uji coba diperoleh bahwa penggunaan multi-feature fusion dengan fitur SIFT dan LEBP memberikan hasil yang relatif lebih baik jika dibandingkan dengan hanya menggunakan fitur tunggal. Hal tersebut dapat dilihat dari peningkatan hasil kinerja sistem pada kondisi optimum dengan nilai akurasi sebesar 97,50%, TPR sebesar 0,9400 dan FPR sebesar 0,0128. ========== Finger vein recognition is one of the areas in the field of biometrics. The steps of finger vein recognition has in common with other biometric recognition process which include image acquisition, preprocessing, feature extraction and matching. The success rate of matching stage is determined by the selection of features. The conditions of finger vein images are susceptible to changes in scale, rotation and translation. The need for features that are resistant to these conditions becomes important. Scale invariant Feature Transform (SIFT) feature is a feature that has been quite widely used for image matching case and be able to withstand degradation due to changes in the condition of the image scale, rotation and translation. However, SIFT feature provide less optimal results when extracted from the image with gray level variations such as those caused by differences in lighting intensity. Local Extensive Binary Pattern (LEBP) feature is a feature that has resistance to gray level variations with richer and discriminatory local characteristics information. Therefore the fusion technique is used to obtain information from SIFT feature and LEBP feature. So that, the feature that has been produced can resist degradation problems such as changes in the condition of the image scale, rotation, translation, and gray level variations which caused by differences in lighting intensity. This study proposes a multi-feature fusion using SIFT and LEBP features for finger vein recognition. This fusion feature will be processed by Learning Vector Quantization (LVQ) method to determine whether the testing image can be x recognized or not. By using a multi-feature fusion, it is expected to get representations of features that can improve the accuracy of the finger vein recognition although the feature is taken from the degraded image. Based on experiment results, finger vein recognition that use multi-feature fusion using integration feature of scale invariant feature transform and local extensive binary pattern provide a better result than only use a single feature. It can be seen from the increase of performance system in optimum condition. The accuracy value can achieve 97.50%, TPR at 0.9400 and FPR at 0.0128

Reconocimiento de expresiones faciales utilizando análisis de componentes principales Kernel (KPCA)

Este artículo presenta una metodología para el reconocimiento de expresiones faciales con análisis de componentes principales kernel, la base de datos utilizada es la Carnegie Mellon University como herramienta de prueba. El método utiliza una función kernel que mapea los datos del espacio característico original a uno de mayor dimensionalidad, de esta forma un problema de origen no lineal se traslada a uno lineal y puede resolverse linealmente, además los métodos basados en kernel pueden reducir el número de parámetros usados para la clasificación, este método es comparado con el análisis de componentes principales y es puesto a discusión donde los porcentajes de acierto encontrados con la base de datos son mayor al 90%.This paper presents a methodology on the recognition of facial expressions with kernel principal component analysis using the Carnegie Mellon University database as a testing tool. This method uses a kernel function to map data from the original feature space to a higher dimensional space, through which a nonlinear problem is translated into a linear one and is to be solved in a linear way, besides a kernel based method can reduce the number of parameters used by the clasiffier, this method compares with principal component analysis and discussed where the percentages of sucess found with the database is greater than 90%

Effective recognition of facial micro-expressions with video motion magnification

Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called micro-expressions. Recognizing a micro-expression is substantially more challenging than conventional expression recognition because these micro-expressions are only temporally exhibited in a fraction of a second and involve minute spatial changes. Until now, work in this field is at a nascent stage, with only a few existing micro-expression databases and methods. In this article, we propose a new micro-expression recognition approach based on the Eulerian motion magnification technique, which could reveal the hidden information and accentuate the subtle changes in micro-expression motion. Validation of our proposal was done on the recently proposed CASME II dataset in comparison with baseline and state-of-the-art methods. We achieve a good recognition accuracy of up to 75.30% by using leave-one-out cross validation evaluation protocol. Extensive experiments on various factors at play further demonstrate the effectiveness of our proposed approach

Wavelet based approach for facial expression recognition

Facial expression recognition is one of the most active fields of research. Many facial expression recognition methods have been developed and implemented. Neural networks (NNs) have capability to undertake such pattern recognition tasks. The key factor of the use of NN is based on its characteristics. It is capable in conducting learning and generalizing, non-linear mapping, and parallel computation. Backpropagation neural networks (BPNNs) are the approach methods that mostly used. In this study, BPNNs were used as classifier to categorize facial expression images into seven-class of expressions which are anger, disgust, fear, happiness, sadness, neutral and surprise. For the purpose of feature extraction tasks, three discrete wavelet transforms were used to decompose images, namely Haar wavelet, Daubechies (4) wavelet and Coiflet (1) wavelet. To analyze the proposed method, a facial expression recognition system was built. The proposed method was tested on static images from JAFFE database

A Comparative Study of Finger Vein Recognition by Using Learning Vector Quantization

¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129

A comparative study of finger vein recognition by using Learning Vector Quantization

Abstract¾ This paper presents a comparative study of finger vein recognition using various features with Learning Vector Quantization (LVQ) as a classification method. For the purpose of this study, two main features are employed: Scale Invariant Feature Transform (SIFT) and Local Extensive Binary Pattern (LEBP). The other features that formed LEBP features: Local Multilayer Binary Pattern (LmBP) and Local Directional Binary Pattern (LdBP) are also employed. The type of images are also become the base of comparison. The SIFT features will be extracted from two types of images which are grayscale and binary images. The feature that have been extracted become the input for recognition stage. In recognition stage, LVQ classifier is used. LVQ will classify the images into two class which are the recognizable images and non recognizable images. The accuracy, false positive rate (FPR), and true positive rate (TPR) value are used to evaluate the performance of finger vein recognition. The performance result of finger vein recognition becomes the main study for comparison stage. From the experiments result, it can be found which feature is the best for finger vein reconition using LVQ. The performance of finger vein recognition that use SIFT feature from binary images give a slightly better result than uisng LmBP, LdBP, or LEBP feature. The accuracy value could achieve 97,45%, TPR at 0,9000 and FPR at 0,0129. 

Reconocimiento de expresiones faciales utilizando análisis de componentes principales kernel (kpca)

Este artículo presenta una metodología para el reconocimiento de expresiones faciales con análisis de componentes principales kernel, la base de datos utilizada es la Carnegie Mellon University como herramienta de prueba. El método utiliza una función kernel que mapea los datos del espacio característico original a uno de mayor dimensionalidad, de esta forma un problema de origen no lineal se traslada a uno lineal y puede resolverse linealmente, además los métodos basados en kernel pueden reducir el número de parámetros usados para la clasificación, este método es comparado con el análisis de componentes principales y es puesto a discusión donde los porcentajes de acierto encontrados con la base de datos son mayor al 90%