• Journal of IKEEE
• /
• v.18 no.1
• /
• pp.1-7
• /
• 2014

In this paper, we suggest an improved Viola-Jones detection framework for the efficient feature selection and the fast rejection method of the sub-window. Our object detector has low computational complexity because it rejects sub-windows until specific threshold. Owing to using same framework, detection performance is same with the existing Viola-Jones detector. We measure the number of average feature calculation about MIT-CMU test set. As a result of the experiment, the number of average feature calculation is reduced to 45.5% and the detection speed is improved about 58.5% compared with the previous algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.22 no.10
• /
• pp.1334-1339
• /
• 2018

Viola & Jones's face detection algorithm is a typical face detection algorithm and shows excellent face detection performance. However, the Viola & Jones's algorithm in images including many faces generates undetected faces and wrong detected faces, such as false faces and duplicate detected faces, due to face diversity. This paper proposes an improved face detection algorithm using a face verification algorithm that eliminates the false detected faces generated from the Viola & Jones's algorithm. The proposed face verification algorithm verifies whether the detected face is valid by evaluating its size, its skin color in the designated area, its edges generated from eyes and mouth, and its duplicate detection. In the face verification experiment of 658 face images detected by the Viola & Jones's algorithm, the proposed face verification algorithm shows that all the face images created in the real person are verified.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2013.11a
• /
• pp.1343-1346
• /
• 2013

본 연구에서는 기존의 Viola-Jones 물체 검출 프레임워크를 개선하여 하나의 특징 당 더 높은 효율을 가지며 검출대상이 아닌 서브 윈도우들을 더 빠르게 제거하는 학습 알고리즘을 제안한다. 학습의 결과로 생성된 물체 검출기는 서브윈도우를 특정 임계값까지 빠르게 제거하기 때문에 서브윈도우당 계산수가 줄어든다. 기존의 Viola-Jones 물체 검출기와 동일한 프레임워크이므로 인식성능에는 영향을 주지 않는다. MIT-CMU 테스트 집합에 대해서 서브윈도우당 특징 계산 횟수를 측정하였으며 기존 계산 횟수의 57%로 줄어들어 검출 속도가 약 71% 향상됨을 확인하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.49 no.3
• /
• pp.18-29
• /
• 2012

Viola-Jones algorithm, a very effective real-time object detection method, uses Haar-like features to constitute weak classifiers. A Haar-like feature is made up of at least two rectangles each of which corresponds to either positive or negative areas and the feature value is computed by subtracting the sum of pixel values in the negative area from that of pixel values in the positive area. Compared to the conventional Haar-like feature which is made up of more than one rectangle, in this paper, we present a couple of new rectangular features whose feature values are computed either by the sum or by the variance of pixel values in a rectangle. By the use of these rectangular features in combination with the conventional Haar-like features, we can select additional features which have been excluded in the conventional Viola-Jones algorithm where every features are the combination of contiguous bright and dark areas of an object. In doing so, we can enhance the performance of object detection without any computational overhead.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2018.05a
• /
• pp.477-480
• /
• 2018

Viola and Jones object detection algorithm is a representative face detection algorithm. The algorithm uses Haar-like features for face expression and uses a cascade-Adaboost algorithm consisting of strong classifiers, a linear combination of weak classifiers for classification. This algorithm requires several parameter settings for its implementation and the set values affect its performance. This paper analyzes face detection performance according to the parameters set in the algorithm.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2014.11a
• /
• pp.917-920
• /
• 2014

본 논문에서는 SWO 방식의 물체 검출기를 개선하여 비디오 환경에 적합하며 실시간 처리가 가능한 Tracking 방식의 PSO 물체 검출기를 제안한다. PSO 방식 스캔은 각각의 입자들이 전역 최적 값으로 수렴하기 때문에 다중 검출에는 적지 않은데, 본 논문에서는 다중 물체를 검출하고 관리할 수 있도록 Tracking 개념을 적용하였다. 제안하는 방법을 적용하면 검출기의 오검출률을 줄이고 안정적인 검출 결과를 얻을 수 있으며 속도가 향상되어 실시간 처리가 가능하다. 논문에서 제안한 알고리즘을 적용해 본 결과 기존의 Viola-Jones 얼굴 검출기와 비교하여 검출률은 동일하면서 속도가 최대 21배 향상되었음을 확인하였다.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.1
• /
• pp.143-147
• /
• 2012

The rapid growth of camera technology can provide various types of information which was not previously provided. Furthermore, IP camera which has rapid data transfer rate and high resolution particularly provide a lot of useful functions beyond the existing simple surveillance capabilities. We are developing Real-Time Face Recognition Access Control System based on the camera technology, and improvement of face detection and recognition algorithms are vitally needed to realize that system. In this paper, we proposes a method to improve the computing speed and detection rate by adding new features to the existing Viola-Jones detection algorithm.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.24 no.3
• /
• pp.378-383
• /
• 2020

Viola & Jones' object detection algorithm is a very good algorithm for the face component(FC) detection, but there are still problems such as duplicate detection, false detection and non-detection due to parameter setting. This paper proposes an improved FC detection algorithm that applies the variable parameter to reduce non-detection and the verification to reduce duplicate detection and false detection to the Viola & Jones' algorithm. The proposed algorithm reduces the non-detection by changing the parameter value of the Viola & Jones' algorithm until the potential valid FCs are detected, and eliminates the duplicate detection and the false detection by using the verification that evaluates size, position, and uniqueness of the detected FCs. Simulation results show that the proposed algorithm includes valid FCs in the detected objects and then detects only the valid FCs by removing invalid FCs from them.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2013.10a
• /
• pp.195-198
• /
• 2013

Recognition and Tracking of interesting object is the significant field in Computer Vision. Mean-Shift algorithm have chronic problems that some errors are occurred when histogram of tracking area is similar to another area. in this paper, we propose to solve the problem. Each algorithm blocks skin color filtering, face detect and Mean-Shift started consecutive order assists better operation of the next algorithm. Avoid to operations of the overhead of tracking area similar to a histogram distribution areas overlap only consider the number of white pixels by running the Viola-Jones algorithm, simple arithmetic increases the convergence of the Mean-Shift. The experimental results, it comes to 78% or more of white pixels in the Mean-Shift search area, only if the recognition of the face area when it is configured to perform a Viola-Jones algorithm is tracking the object, was 100 percent successful.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.26 no.6
• /
• pp.464-470
• /
• 2016

In this paper, we propose an improved face detection algorithm and determination method for drowsiness status of driver from the opening and closing frequency of the detected eye. For this purpose, face, eyes, nose, and mouth are detected based on conventional Viola-Jones face detection algorithm and spatial correlation of face. Here the spatial correlation of face is performed by DFP(Detect Face Part) based on seven characteristics. The experimental results on Caltect face image database revealed that the detection rates of noise particularly showed the improved performance of 13.78% in comparison to that of the previous Viola-Jones algorithm. Furthermore, we analyze the driver's drowsiness determination cumulative value of the eye closed state as a function of time based on SVM (Support Vector Machine) and PERCLOS(Percentage Closure of Eyes). The experimental results confirmed the usefulness of the proposed method by obtaining a driver's drowsiness determination rate of 93.28%.