Robust Face Recognition by Computing Distances From Multiple Histograms of Oriented Gradients

Karaaba, Mahir Faik; Surinta, Olarik; Schomaker, Lambert; Wiering, Marco A.

doi:10.1109/ssci.2015.39

Cited by 18 publications

(10 citation statements)

References 30 publications

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“…This method has been successfully applied to many computer vision problems using human body or face images, such as the pedestrian detection [39], age estimation [40], face recognition [41], gender recognition [42,43]. The principle of the HOG method is that the HOG method constructs histogram features of a sub-block of an image by accumulating the strength and direction of the gradient information at every pixel inside the sub-block.…”

Section: Proposed Methods For Person Recognition Using Visible Lighmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

440

159

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The human body contains identity information that can be used for the person recognition (verification/recognition) problem. In this paper, we propose a person recognition method using the information extracted from body images. Our research is novel in the following three ways compared to previous studies. First, we use the images of human body for recognizing individuals. To overcome the limitations of previous studies on body-based person recognition that use only visible light images for recognition, we use human body images captured by two different kinds of camera, including a visible light camera and a thermal camera. The use of two different kinds of body image helps us to reduce the effects of noise, background, and variation in the appearance of a human body. Second, we apply a state-of-the art method, called convolutional neural network (CNN) among various available methods, for image features extraction in order to overcome the limitations of traditional hand-designed image feature extraction methods. Finally, with the extracted image features from body images, the recognition task is performed by measuring the distance between the input and enrolled samples. The experimental results show that the proposed method is efficient for enhancing recognition accuracy compared to systems that use only visible light or thermal images of the human body.

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Section: Proposed Methods For Person Recognition Using Visible Lighmentioning

confidence: 99%

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Nguyen

Hong

Kim

et al. 2017

Sensors

440

159

View full text Add to dashboard Cite

show abstract

“…As the feature extractor, the HOG method has been widely applied to various computer vision problems using face images or human body, such as the pedestrian detection [ 16 , 42 ], age estimation [ 43 ], face recognition [ 44 ], and gender recognition [ 45 , 46 ]. The HOG method constructs histogram features of a sub-block of an image based on the accumulation of the direction and strength of the gradient information at every pixel within the sub-block.…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Kim

Hong

Park

2017

Sensors

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Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

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“…Today there are many advantages for digital signature such as offers more security than any electronic signature, independent verification cannot be alter by unauthorized parties and long-term retention and access. Face recognition and digital signature have been a long-standing issue in PC vision [7]. As of late, Histograms of Oriented Gradients (HOGs) have turned out to be an effective descriptor as feature extraction for object recognition in general and face recognition and digital signature in particular.…”

Section: Introductionmentioning

confidence: 99%

An Accurate Multi-Biometric Personal Identification Model using Histogram of Oriented Gradients (HOG)

Ahmad¹,

Ismail²,

Omer³

2018

ijacsa

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Biometrics is the detection and description of individuals' physiological and behavioral features. Many different systems require reliable personal identification schemes to either prove or find out the identity of an individual demanding their services. Multi-biometrics are required inside the current context of large worldwide biometric databases and to provide new developing security demands. There are some distinctive and measurable features used to distinguish individuals known as Biometric Identifiers. Multi-biometric systems tend to integrate multiple identifiers to increase recognition accuracy. Face and digital signature identifiers are still a challenge in many applications, especially in security systems. The fundamental objective of this paper is to integrate both identifiers in an accurate personal identification model. In this paper, a reliable multi-biometric model based on Histogram of Oriented Gradients (HOG) features of a face and digital signature and is able to identify individuals accurately is proposed. The methodology is to adopt many parameters such as weights of HOG features in merging process, the HOG parameters itself, and the distance method in matching process to gain higher accuracy. The proposed model achieves perfect results in personal identification using HOG features of digital signature and face together. The results show that the HOG feature descriptor significantly performs target matching at an average of 100% accuracy ratio for face recognition together with the digital signature. It outperforms existing feature sets with an accuracy of 84.25% for face only and 97.42% for digital signature only.

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Robust Face Recognition by Computing Distances From Multiple Histograms of Oriented Gradients

Cited by 18 publications

References 30 publications

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Person Recognition System Based on a Combination of Body Images from Visible Light and Thermal Cameras

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

An Accurate Multi-Biometric Personal Identification Model using Histogram of Oriented Gradients (HOG)

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