Yakup Demir

Bio: Yakup Demir is an academic researcher from Fırat University. The author has contributed to research in topics: Feature extraction & Support vector machine. The author has an hindex of 17, co-authored 48 publications receiving 1234 citations.

Face recognition based on convolutional neural network

Abstract: Face recognition is of great importance to real world applications such as video surveillance, human machine interaction and security systems. As compared to traditional machine learning approaches, deep learning based methods have shown better performances in terms of accuracy and speed of processing in image recognition. This paper proposes a modified Convolutional Neural Network (CNN) architecture by adding two normalization operations to two of the layers. The normalization operation which is batch normalization provided acceleration of the network. CNN architecture was employed to extract distinctive face features and Softmax classifier was used to classify faces in the fully connected layer of CNN. In the experiment part, Georgia Tech Database showed that the proposed approach has improved the face recognition performance with better recognition results.

A new facial expression recognition based on curvelet transform and online sequential extreme learning machine initialized with spherical clustering

Abstract: In this paper, a novel algorithm is proposed for facial expression recognition by integrating curvelet transform and online sequential extreme learning machine (OSELM) with radial basis function (RBF) hidden node having optimal network architecture. In the proposed algorithm, the curvelet transform is firstly applied to each region of the face image divided into local regions instead of whole face image to reduce the curvelet coefficients too huge to classify. Feature set is then generated by calculating the entropy, the standard deviation and the mean of curvelet coefficients of each region. Finally, spherical clustering (SC) method is employed to the feature set to automatically determine the optimal hidden node number and RBF hidden node parameters of OSELM by aim of increasing classification accuracy and reducing the required time to select the hidden node number. So, the learning machine is called as OSELM-SC. It is constructed two groups of experiments: The aim of the first one is to evaluate the classification performance of OSELM-SC on the benchmark datasets, i.e., image segment, satellite image and DNA. The second one is to test the performance of the proposed facial expression recognition algorithm on the Japanese Female Facial Expression database and the Cohn-Kanade database. The obtained experimental results are compared against the state-of-the-art methods. The results demonstrate that the proposed algorithm can produce effective facial expression features and exhibit good recognition accuracy and robustness.

Object recognition and detection with deep learning for autonomous driving applications

Abstract: Autonomous driving requires reliable and accurate detection and recognition of surrounding objects in real drivable environments Although different object detection algorithms have been proposed, not all are robust enough to detect and recognize occluded or truncated objects In this paper, we propose a novel hybrid Local Multiple system LM-CNN-SVM based on Convolutional Neural Networks CNNs and Support Vector Machines SVMs due to their powerful feature extraction capability and robust classification property, respectively In the proposed system, we divide first the whole image into local regions and employ multiple CNNs to learn local object features Secondly, we select discriminative features by using Principal Component Analysis We then import into multiple SVMs applying both empirical and structural risk minimization instead of using a direct CNN to increase the generalization ability of the classifier system Finally, we fuse SVM outputs In addition, we use the pre-trained AlexNet and a new CNN architecture We carry out object recognition and pedestrian detection experiments on the Caltech-101 and Caltech Pedestrian datasets Comparisons to the best state-of-the-art methods show that the proposed system achieved better results

Activation Functions: Comparison of trends in Practice and Research for Deep Learning

Abstract: Deep neural networks have been successfully used in diverse emerging domains to solve real world complex problems with may more deep learning(DL) architectures, being developed to date. To achieve these state-of-the-art performances, the DL architectures use activation functions (AFs), to perform diverse computations between the hidden layers and the output layers of any given DL architecture. This paper presents a survey on the existing AFs used in deep learning applications and highlights the recent trends in the use of the activation functions for deep learning applications. The novelty of this paper is that it compiles majority of the AFs used in DL and outlines the current trends in the applications and usage of these functions in practical deep learning deployments against the state-of-the-art research results. This compilation will aid in making effective decisions in the choice of the most suitable and appropriate activation function for any given application, ready for deployment. This paper is timely because most research papers on AF highlights similar works and results while this paper will be the first, to compile the trends in AF applications in practice against the research results from literature, found in deep learning research to date.