A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection

Sharifara, Ali; Rahim, Mohd Shafry Mohd; Anisi, Yasaman

doi:10.1109/isbast.2014.7013097

Cited by 80 publications

(27 citation statements)

References 12 publications

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“…The cascade object detector used integral image representation for fast feature extraction 16 from original images, and it trained simple and efficient classifiers using the adaptive boosting (AdaBoost) method 8 to select a small number of important features. Successively, more complex classifiers were then combined in a cascade structure, which dramatically increased the detection speed by focusing attention on promising regions of the image.…”

Section: Model Trainingmentioning

confidence: 99%

“…8,13 More recently, convolutional neural networks (CNNs) 14 have emerged as a very powerful approach to image recognition tasks. [15][16][17] A CNN -a kind of artificial neural network that operates convolution directly on raw pixel intensity data-consists of several repeating layers of convolution, nonlinearity, and pooling, followed by fully connected layers. 18 CNN is a rapidly developing field, and new CNN structures are still being proposed.…”

Section: Introductionmentioning

confidence: 99%

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Automated defect analysis in electron microscopic images

2018

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Electron microscopy and defect analysis are a cornerstone of materials science, as they offer detailed insights on the microstructure and performance of a wide range of materials and material systems. Building a robust and flexible platform for automated defect recognition and classification in electron microscopy will result in the completion of analysis orders of magnitude faster after images are recorded, or even online during image acquisition. Automated analysis has the potential to be significantly more efficient, accurate, and repeatable than human analysis, and it can scale with the increasingly important methods of automated data generation. Herein, an automated recognition tool is developed based on a computer vison-based approach; it sequentially applies a cascade object detector, convolutional neural network, and local image analysis methods. We demonstrate that the automated tool performs as well as or better than manual human detection in terms of recall and precision and achieves quantitative image/defect analysis metrics close to the human average. The proposed approach works for images of varying contrast, brightness, and magnification. These promising results suggest that this and similar approaches are worth exploring for detecting multiple defect types and have the potential to locate, classify, and measure quantitative features for a range of defect types, materials, and electron microscopic techniques.

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Section: Model Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated defect analysis in electron microscopic images

2018

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“…Due to this, sometimes an eye, partial nose cannot be viewed properly [13]. Hence that may appear in half profile and would rather make it easy to detect the face.…”

Section: A Head Posementioning

confidence: 99%

Real Time Face Detection Using Matlab

Fathema¹,

Lakshmi²,

Ravali³

et al. 2018

IJERT

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Abstract---This electronic document is about face detection. In computer literature face detection has been one of the most studied topics. Given an arbitrary image, the goal of this project is to determine whether or not there are any faces in the image and detection of eyes and upperbody. While this appears to be a trivial task for human beings, it is very challenging task for computers. The difficulty associated with face detection can be attributed to many variations in scale, location, view point, illumination, occlusions, etc. Although there have been hundreds of reports reported approaches for face detection, if one were asked to name a single face detection algorithm that has most impact in recent decades, it will most likely be the Viola and Jones face detection, which is capable of processing images extremely rapidly and achieve high detection rates. In thiswe are going to study and understand the ViolaJones algorithm by implementing the detection frame work and based on that implementation, conduct experiment to improve the performance.

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“…Most practical applications are able to increase the detection rate, and false alarm rate accordingly. This is due to the decision threshold that is taught to distinguish between face and non-face under training data set [20].…”

Section: Figure 2 An Example Of Face Detector Output With Low Accuracymentioning

confidence: 99%

Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection

Sharifara

Rahim

Navabifar

et al. 2017

Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments

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Face detection is one of the challenging tasks in computer vision. Human face detection plays an essential role in the first stage of face processing applications such as face recognition, face tracking, image database management, etc. In these applications, face objects often come from an inconsequential part of images that contain variations, namely different illumination, poses, and occlusion. These variations can decrease face detection rate noticeably. Most existing face detection approaches are not accurate, as they have not been able to resolve unstructured images due to large appearance variations and can only detect human faces under one particular variation. Existing frameworks of face detection need enhancements to detect human faces under the stated variations to improve detection rate and reduce detection time. In this study, an enhanced face detection framework is proposed to improve detection rate based on skin color and provide a validation process. A preliminary segmentation of the input images based on skin color can significantly reduce search space and accelerate the process of human face detection. The primary detection is based on Haar-like features and the Adaboost algorithm. A validation process is introduced to reject non-face objects, which might occur during the face detection process. The validation process is based on two-stage Extended Local Binary Patterns. The experimental results on the CMU-MIT and Caltech 10000 datasets over a wide range of facial variations in different colors, positions, scales, and lighting conditions indicated a successful face detection rate. CCS Concepts • Computing methodologies~Computer vision• Computing methodologies~Machine learning algorithms • Computing methodologies~Image manipulation • Theory of computation~Design and analysis of algorithms

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A general review of human face detection including a study of neural networks and Haar feature-based cascade classifier in face detection

Cited by 80 publications

References 12 publications

Automated defect analysis in electron microscopic images

Automated defect analysis in electron microscopic images

Real Time Face Detection Using Matlab

Enhanced Facial Recognition Framework based on Skin Tone and False Alarm Rejection

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