IMPLEMENTASI ALGORITMA EXTREME LEARNING MACHINE PADA PREDIKSI AKTIVITAS BADAI GEOMAGNETIK

Badai geomagnetik merupakan gangguan yang terjadi di magnetosfer bumi, akibat adanya aktivitas dari matahari. Dalam rangka peringatan dini, Lembaga Penerbangan dan Antariksa Nasional (LAPAN) di Indonesia memiliki kegiatan rutin untuk memprediksi kemungkinan terjadinya badai tersebut dalam rentang waktu 24 jam ke depan. Namun pada tahun 2015, hasil prediksi badai geomagnetik yang dilakukan secara manual oleh LAPAN hanya mendapatkan akurasi sebesar 57,14%. Oleh karena itu, penelitian ini mengusulkan pemanfaatan metode Extreme Learning Machine (ELM) dalam melakukan prediksi badai geomagnetik, dengan tujuan untuk mendapatkan akurasi yang lebih baik. Data penelitian yang digunakan meliputi data coronal hole, coronal mass ejection, solar wind dan indeks Dst pada tahun 2011 hingga 2016. Hasil penelitian ini menunjukkan bahwa algoritma ELM memiliki tingkat akurasi yang lebih besar dalam memprediksi badai geomagnetik tahun 2015, dengan perolehan nilai 57,80822%. Meskipun memiliki selisih akurasi yang kecil, namun pemanfaatan ELM ini dapat membantu prediksi badai geomagnetik secara otomatis. Secara umum, algoritma ELM yang dibangun dalam penelitian ini memiliki nilai rata-rata akurasi prediksi tertinggi sebesar 69,9055%.---------- The geomagnetic storm is a disturbance that occurs in the earth’s magnetosphere, as the result of the activity of the sun. In case for early warning, National Institute of Aeronautics and Space Agency (LAPAN) in Indonesia has a routine activity to predict the probability of geomagnetic storm appearance for the next 24 hours. But in 2015, the geomagnetic storm prediction results are done manually just managed to get the accuracy of 57.14%. Therefore, this research proposes the utilization method of Extreme Learning Machine (ELM) for geomagnetic storm prediction, in order to get better accuracy. Research data that used include data on coronal holes, coronal mass ejection, solar wind and the Dst index from 2011 to 2016. The results of this research show that the ELM algorithm has a greater accuracy in prediction the 2015 geomagnetic storm activity, with the acquisition of 57.80822% value. Despite the difference in accuracy is small, but the utilization of ELM can help predicting geomagnetic storm automatically. In general, the ELM algorithm built in this research have the average value of the highest prediction accuracy of 69.9055%

Ear Biometrics: A Comprehensive Study of Taxonomy, Detection, and Recognition Methods

Due to the recent challenges in access control, surveillance and security, there is an increased need for efficient human authentication solutions. Ear recognition is an appealing choice to identify individuals in controlled or challenging environments. The outer part of the ear demonstrates high discriminative information across individuals and has shown to be robust for recognition. In addition, the data acquisition procedure is contactless, non-intrusive, and covert. This work focuses on using ear images for human authentication in visible and thermal spectrums. We perform a systematic study of the ear features and propose a taxonomy for them. Also, we investigate the parts of the head side view that provides distinctive identity cues. Following, we study the different modules of the ear recognition system. First, we propose an ear detection system that uses deep learning models. Second, we compare machine learning methods to state traditional systems\u27 baseline ear recognition performance. Third, we explore convolutional neural networks for ear recognition and the optimum learning process setting. Fourth, we systematically evaluate the performance in the presence of pose variation or various image artifacts, which commonly occur in real-life recognition applications, to identify the robustness of the proposed ear recognition models. Additionally, we design an efficient ear image quality assessment tool to guide the ear recognition system. Finally, we extend our work for ear recognition in the long-wave infrared domains

Local Higher-Order Statistics (LHS) describing images with statistics of local non-binarized pixel patterns

Accepted for publication in International Journal of Computer Vision and Image Understanding (CVIU)International audienceWe propose a new image representation for texture categorization and facial analysis, relying on the use of higher-order local differential statistics as features. It has been recently shown that small local pixel pattern distributions can be highly discriminative while being extremely efficient to compute, which is in contrast to the models based on the global structure of images. Motivated by such works, we propose to use higher-order statistics of local non-binarized pixel patterns for the image description. The proposed model does not require either (i) user specified quantization of the space (of pixel patterns) or (ii) any heuristics for discarding low occupancy volumes of the space. We propose to use a data driven soft quantization of the space, with parametric mixture models, combined with higher-order statistics, based on Fisher scores. We demonstrate that this leads to a more expressive representation which, when combined with discriminatively learned classifiers and metrics, achieves state-of-the-art performance on challenging texture and facial analysis datasets, in low complexity setup. Further, it is complementary to higher complexity features and when combined with them improves performance

Automated Face Recognition: Challenges and Solutions

Automated face recognition (AFR) aims to identify people in images or videos using pattern recognition techniques. Automated face recognition is widely used in applications ranging from social media to advanced authentication systems. Whilst techniques for face recognition are well established, the automatic recognition of faces captured by digital cameras in unconstrained, real‐world environment is still very challenging, since it involves important variations in both acquisition conditions as well as in facial expressions and in pose changes. Thus, this chapter introduces the topic of computer automated face recognition in light of the main challenges in that research field and the developed solutions and applications based on image processing and artificial intelligence methods

Grassmann Learning for Recognition and Classification

Computational performance associated with high-dimensional data is a common challenge for real-world classification and recognition systems. Subspace learning has received considerable attention as a means of finding an efficient low-dimensional representation that leads to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces, where points represent subspaces and the relationship between points is defined by a mapping of an orthogonal matrix. Grassmann learning involves embedding high dimensional subspaces and kernelizing the embedding onto a projection space where distance computations can be effectively performed. In this dissertation, Grassmann learning and its benefits towards action classification and face recognition in terms of accuracy and performance are investigated and evaluated. Grassmannian Sparse Representation (GSR) and Grassmannian Spectral Regression (GRASP) are proposed as Grassmann inspired subspace learning algorithms. GSR is a novel subspace learning algorithm that combines the benefits of Grassmann manifolds with sparse representations using least squares loss §¤1-norm minimization for improved classification. GRASP is a novel subspace learning algorithm that leverages the benefits of Grassmann manifolds and Spectral Regression in a framework that supports high discrimination between classes and achieves computational benefits by using manifold modeling and avoiding eigen-decomposition. The effectiveness of GSR and GRASP is demonstrated for computationally intensive classification problems: (a) multi-view action classification using the IXMAS Multi-View dataset, the i3DPost Multi-View dataset, and the WVU Multi-View dataset, (b) 3D action classification using the MSRAction3D dataset and MSRGesture3D dataset, and (c) face recognition using the ATT Face Database, Labeled Faces in the Wild (LFW), and the Extended Yale Face Database B (YALE). Additional contributions include the definition of Motion History Surfaces (MHS) and Motion Depth Surfaces (MDS) as descriptors suitable for activity representations in video sequences and 3D depth sequences. An in-depth analysis of Grassmann metrics is applied on high dimensional data with different levels of noise and data distributions which reveals that standardized Grassmann kernels are favorable over geodesic metrics on a Grassmann manifold. Finally, an extensive performance analysis is made that supports Grassmann subspace learning as an effective approach for classification and recognition

Archetypes for histogram-valued data

Il principale sviluppo innovativo del lavoro è quello di propone una estensione dell'analisi archetipale per dati ad istogramma. Per quanto concerne l'impianto metodologico nell'approccio all'analisi di dati ad istogramma, che sono di natura complessa, il presente lavora utilizza le intuizioni della "Symbolic Data Analysis" (SDA) e le relazioni intrinseche tra dati valutati ad intervallo e dati valutati ad istogramma. Dopo aver discusso la tecnica sviluppata in ambiente Matlab, il suo funzionamento e le sue proprietà su di un esempio di comodo, tale tecnica viene proposta, nella sezione applicativa, come strumento per effettuare una analisi di tipo "benchmarking" quantitativo. Nello specifico, si propongono i principali risultati ottenuti da una applicazione degli archetipi per dati ad istogramma ad un caso di benchmarking interno del sistema scolastico, utilizzando dati provenienti dal test INVALSI relativi all'anno scolastico 2015/2016. In questo contesto l'unità di analisi è considerata essere la singola scuola, definita operativamente attraverso le distribuzioni dei punteggi dei propri alunni valutate, congiuntamente, sotto forma di oggetti simbolici ad istogramma

Hierarchical age estimation using enhanced facial features.

Doctor of Philosopy in Computer Science, University of KwaZulu-Natal, Westville, 2018.Ageing is a stochastic, inevitable and uncontrollable process that constantly affect shape, texture and general appearance of the human face. Humans can easily determine ones’ gender, identity and ethnicity with highest accuracy as compared to age. This makes development of automatic age estimation techniques that surpass human performance an attractive yet challenging task. Automatic age estimation requires extraction of robust and reliable age discriminative features. Local binary patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age discriminative features. Although local ternary patterns (LTP) is insensitive to noise, it uses a single static threshold for all images regardless of varied image conditions. Local directional patterns (LDP) uses k directional responses to encode image gradient and disregards not only central pixel in the local neighborhood but also 8 k directional responses. Every pixel in an image carry subtle information. Discarding 8 k directional responses lead to lose of discriminative texture features. This study proposes two variations of LDP operator for texture extraction. Significantorientation response LDP (SOR-LDP) encodes image gradient by grouping eight directional responses into four pairs. Each pair represents orientation of an edge with respect to central reference pixel. Values in each pair are compared and the bit corresponding to the maximum value in the pair is set to 1 while the other is set to 0. The resultant binary code is converted to decimal and assigned to the central pixel as its’ SOR-LDP code. Texture features are contained in the histogram of SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the difference between neighboring pixels and central pixel in 3 3 image region. These differential values are convolved with Kirsch edge detectors to obtain directional responses. These responses are normalized and used as probability of an edge occurring towards a respective direction. An adaptive threshold is applied to derive LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms of negative and positive LTDP encoded images are concatenated to obtain texture feature. Regardless of there being evidence of spatial frequency processing in primary visual cortex, biologically inspired features (BIF) that model visual cortex uses only scale and orientation selectivity in feature extraction. Furthermore, these BIF are extracted using holistic (global) pooling across scale and orientations leading to lose of substantive information. This study proposes multi-frequency BIF (MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol, this study investigated performance of proposed feature extractors in age estimation in a hierarchical way by performing age-group classification using Multi-layer Perceptron (MLP) followed by within age-group exact age regression using support vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were used to evaluate performance of proposed face descriptors. Experimental results on FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform state-of-the-art feature descriptors in age estimation. Experimental results show that performing gender discrimination before age-group and age estimation further improves age estimation accuracies. Shape, appearance, wrinkle and texture features are simultaneously extracted by visual system in primates for the brain to process and understand an image or a scene. However, age estimation systems in the literature use a single feature for age estimation. A single feature is not sufficient enough to capture subtle age discriminative traits due to stochastic and personalized nature of ageing. This study propose fusion of different facial features to enhance their discriminative power. Experimental results show that fusing shape, texture, wrinkle and appearance result into robust age discriminative features that achieve lower MAE compared to single feature performance

Theoretical and methodological advances in semi-supervised learning and the class-imbalance problem.

201 p.Este trabajo se centra en la generalización teórica y práctica de dos situaciones desafiantes y conocidas del campo del aprendizaje automático a problemas de clasificación en los cuales la suposición de tener una única clase binaria no se cumple.Aprendizaje semi-supervisado es una técnica que usa grandes cantidades de datos no etiquetados para, así, mejorar el rendimiento del aprendizaje supervisado cuando el conjunto de datos etiquetados es muy acotado. Concretamente, este trabajo contribuye con metodologías potentes y computacionalmente eficientes para aprender, de forma semi-supervisada, clasificadores para múltiples variables clase. También, se investigan, de forma teórica, los límites fundamentales del aprendizaje semi-supervisado en problemas multiclase.El problema de desbalanceo de clases aparece cuando las variables objetivo presentan una distribución de probabilidad lo suficientemente desbalanceada como para desvirtuar las soluciones propuestas por los algoritmos de aprendizaje supervisado tradicionales. En este proyecto, se propone un marco teórico para separar la desvirtuación producida por el desbalanceo de clases de otros factores que afectan a la precisión de los clasificadores. Este marco es usado principalmente para realizar una recomendación de métricas de evaluación de clasificadores en esta situación. Por último, también se propone una medida del grado de desbalanceo de clases en un conjunto de datos correlacionada con la pérdida de precisión ocasionada.Intelligent Systems Grou