A survey of techniques for human detection in static images

Santhanam, T.; Sumathi, C. P.; Gomathi, S.

doi:10.1145/2393216.2393272

Cited by 6 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The realization of various face detection algorithms has paved way to the exponential rise in the development of numerous face related applications such as authentication system [2], surveillance system [3], emotion recognition[7], [8], iris detection system [38]], speech production application [6], automated attendance system [1] driver fatigue detection [14] etc. Some of the common face detection algorithms used in these applications include; Support Vector Machine (SVM), local Binary Pattern (LPB), Ada boost, Eigen faces, template matching, neural networks, Viola Jones, Principle Component Analysis (PCA) [5], [11] and [12]. These face detection algorithms are generally classified into four key categories [10], [15], [19], [37] a n d [38]:  Knowledge based methods-Top down approach Knowledge based methods use rules of thumb (heuristic rules) to detect the face region.…”

Section: Categories Of Face Detection Feature Extraction Algorithmsmentioning

confidence: 99%

“…The term "face detection" and "face recognition" are usually used interchangeably but they have different meaning. Face detection and face recognition are both complex computer vision tasks [2]; but face detection is normally the first step in many face related applications [5] that identifies existence, site and dimension of human faces in digital images [10], while face recognition consists of mainly two phases, initially face detection followed by face recognition i.e. matching the detected face with an existing face image in a database [22], [23].…”

Section: Introductionmentioning

confidence: 99%

“…Classification Pre-processing comprises of several operations such as scaling, resizing, normalization, while feature extraction is achieved by transformation approaches such as LBP, Gabor wavelets, PCA [23] and lastly classification using SVM, NN, Distance classifiers, HMM model etc. [5], [11], [12] , [37] and [23]. This paper is structured as follows: Section 2 and 3 focuses on presenting a short description of face detection algorithms categories and drawbacks of existing algorithms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploration of Face Detection methods in Digital Images

Ruvinga*,

Malathi,

Jayaseeli

2019

IJRTE

View full text Add to dashboard Cite

Face detection is a challenging computer vision task that identifies and localizes the faces of human beings from digital images or video streams. It is predominantly the first phase in the process of developing a wide range of face applications such as face recognition, emotion recognition, authentication, surveillance systems etc. The process of face detection is easy from the human perspective but, a complex task for computers that involves searching of the face in variable circumstances of pose, colour, size, occlusion, illumination etc. If the outcome of face detection is intended to be input for another algorithm, an accurate, well informed selection of an appropriate face detection technique is essential because the overall performance of face application is dependent on face detection algorithm’s precision. The survey paper presents a review of three commonly used face detection algorithms available in literature namely Viola Jones, Neural networks (NN) and Local Binary Pattern (LBP) for the purpose of ascertaining the most suitable face detection algorithm to implement for our future work in developing an ‘Online student concentration level recognition system’.

show abstract

Section: Categories Of Face Detection Feature Extraction Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Exploration of Face Detection methods in Digital Images

Ruvinga*,

Malathi,

Jayaseeli

2019

IJRTE

View full text Add to dashboard Cite

show abstract

“…We have observed only one short descriptive review so far on human detection [49]. Nevertheless, the article did not satisfy the descriptive review requirements.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Human Detection Techniques: A Descriptive Review

Sumit

Mirjalili

2021

IEEE Access

View full text Add to dashboard Cite

“…Human detection methods can be divided into video based [7] or static image based [8]. The major difference between these two types of methods is that video based approaches could also utilise the motion features such as background subtraction [9] and optical flow [10], this is impracticable with a single image.…”

Section: Introductionmentioning

confidence: 99%

Human Detection Using Gradient Maps and Golden Ratio

Su¹,

Fang²

2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

View full text Add to dashboard Cite

-Human detection is an important topic that can be used for many applications, it is mainly found in areas that required surveillance such as airports, casinos, factories, construction and mining sites. In this paper, a novel human detection method is introduced to extract the human figure from an input image without prior information or training. This method firstly uses a head and shoulder detection scheme based on curve detection with scaled gradient magnitude and orientation maps. It is then followed by a human body estimation scheme based on gap detection and golden ratio. Finally, the human figure is extracted through thresholding local gradient magnitude regions and horizontal filling. Tests on various images have shown that this method is capable of detecting and extracting human body figures robustly from different images.

show abstract

A survey of techniques for human detection in static images

Cited by 6 publications

References 23 publications

Exploration of Face Detection methods in Digital Images

Exploration of Face Detection methods in Digital Images

Vision-Based Human Detection Techniques: A Descriptive Review

Human Detection Using Gradient Maps and Golden Ratio

Contact Info

Product

Resources

About