Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.

Facial recognition using histogram of gradients and support vector machines

Abstract: Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.

Histogram of oriented velocities for eye movement detection

Abstract: Research in various fields including psychology, cognition, and medical science deal with eye tracking data to extract information about the intention and cognitive state of a subject. For the extraction of this information, the detection of eye movement types is an important task. Modern eye tracking data is noisy and most of the state-of-the-art algorithms are not developed for all types of eye movements since they are still under research. We propose a novel feature for eye movement detection, which is called histogram of oriented velocities. The construction of the feature is similar to the well known histogram of oriented gradients from computer vision. Since the detector is trained using machine learning, it can always be extended to new eye movement types. We evaluate our feature against the state-of-the-art on publicly available data. The evaluation includes different machine learning approaches such as support vector machines, regression trees, and k nearest neighbors. We evaluate our feature together with the machine learning approaches for different parameter sets. We provide a matlab script for the computation and evaluation as well as an integration in EyeTrace which can be downloaded at http://www.ti.uni-tuebingen.de/Eyetrace.1751.0.html.

An Effective Palmprint Recognition Approach for Visible and Multispectral Sensor Images

Abstract: Among several palmprint feature extraction methods the HOG-based method is attractive and performs well against changes in illumination and shadowing of palmprint images. However, it still lacks the robustness to extract the palmprint features at different rotation angles. To solve this problem, this paper presents a hybrid feature extraction method, named HOG-SGF that combines the histogram of oriented gradients (HOG) with a steerable Gaussian filter (SGF) to develop an effective palmprint recognition approach. The approach starts by processing all palmprint images by David Zhang’s method to segment only the region of interests. Next, we extracted palmprint features based on the hybrid HOG-SGF feature extraction method. Then, an optimized auto-encoder (AE) was utilized to reduce the dimensionality of the extracted features. Finally, a fast and robust regularized extreme learning machine (RELM) was applied for the classification task. In the evaluation phase of the proposed approach, a number of experiments were conducted on three publicly available palmprint databases, namely MS-PolyU of multispectral palmprint images and CASIA and Tongji of contactless palmprint images. Experimentally, the results reveal that the proposed approach outperforms the existing state-of-the-art approaches even when a small number of training samples are used.

Histogram of Oriented Gradients for writer's gender, handedness and age prediction

Abstract: This work focuses on automatic prediction of the writer's biometrics including gender, handedness and age information The proposed prediction system is based on the use of Histogram of Oriented Gradients (HOG), which aims to extract gradient directions from the handwritten text The prediction task is achieved using SVM classifier Experiments performed on IAM and KHATT datasets, reveal promising results Precisely, the comparison with the state of the art shows that, the proposed prediction system provides quite encouraging performance