scholarly journals Face Detection Ensemble with Methods Using Depth Information to Filter False Positives

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5242
Author(s):  
Loris Nanni ◽  
Sheryl Brahnam ◽  
Alessandra Lumini
Keyword(s):  
Computer Vision ◽  
Face Detection ◽  
Detection Rate ◽  
Fundamental Problem ◽  
Depth Map ◽  
False Positives ◽  
Experimental Results ◽  
Depth Information ◽  
Depth Data ◽  
Filtering Approach

A fundamental problem in computer vision is face detection. In this paper, an experimentally derived ensemble made by a set of six face detectors is presented that maximizes the number of true positives while simultaneously reducing the number of false positives produced by the ensemble. False positives are removed using different filtering steps based primarily on the characteristics of the depth map related to the subwindows of the whole image that contain candidate faces. A new filtering approach based on processing the image with different wavelets is also proposed here. The experimental results show that the applied filtering steps used in our best ensemble reduce the number of false positives without decreasing the detection rate. This finding is validated on a combined dataset composed of four others for a total of 549 images, including 614 upright frontal faces acquired in unconstrained environments. The dataset provides both 2D and depth data. For further validation, the proposed ensemble is tested on the well-known BioID benchmark dataset, where it obtains a 100% detection rate with an acceptable number of false positives.

FACE DETECTION FROM GREYSCALE IMAGES USING DETAILS FROM CATEGORIZED WAVELET COEFFICIENTS AS FEATURES FOR A DYNAMIC SUPERVISED FORWARD PROPAGATION NETWORK

2009 ◽  
Vol 23 (01) ◽  
pp. 3-15 ◽  
Author(s):  
LEE SENG YEONG ◽  
LI-MINN ANG ◽  
KING HANN LIM ◽  
KAH PHOOI SENG
Keyword(s):  
Face Detection ◽  
Detection Rate ◽  
Image Data ◽  
False Positives ◽  
Experimental Results ◽  
Training Data ◽  
Correct Detection ◽  
Wavelet Coefficients ◽  
Propagation Network ◽  
Counterpropagation Network

A dynamic counterpropagation network based on the forward only counterpropagation network (CPN) is applied as the classifier for face detection. The network, called the dynamic supervised forward-propagation network (DSFPN) trains using a supervised algorithm that grows dynamically during training allowing subclasses in the training data to be learnt. The network is trained using a reduced dimensionality categorized wavelet coefficients of the image data. Experimental results obtained show that a 94% correct detection rate can be achieved with less than 6% false positives.

Robust Face Detection for Low-Resolution Images

2006 ◽  
Vol 10 (1) ◽  
pp. 93-101 ◽  
Author(s):  
Shinji Hayashi ◽  
◽  
Osamu Hasegawa ◽  
Keyword(s):  
Computer Vision ◽  
Face Detection ◽  
Frequency Band ◽  
Detection Rate ◽  
False Positives ◽  
Low Resolution ◽  
Test Set ◽  
Standard Detector ◽  
Face Detector ◽  
Low Resolution Images

Face detection, one of the most actively researched and progressive computer vision fields, has been little studied in low-resolution images. Using the AdaBoost-based face detector and MIT+CMU frontal face test set – the standard detector and images for evaluation in face detection – we found that face detection rate falls to 39% from 88% as face resolution decreases from 24×24 pixels to 6×6 pixels. We discuss a proposal using “portrait images,” “image expansion,” “frequency-band limitation of features” and “two-detector integration” and show that 71% of face detection rate is obtained for 6×6 pixel faces of MIT+CMU frontal face test set. Note that each of the above detections involves 100 false positives for 112 evaluation images.

scholarly journals EANet: Depth Estimation Based on EPI of Light Field

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yunzhang Du ◽  
Qian Zhang ◽  
Dingkang Hua ◽  
Jiaqi Hou ◽  
Bin Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Medical Treatment ◽  
Light Field ◽  
Spatial Information ◽  
Depth Map ◽  
Depth Estimation ◽  
Experimental Results ◽  
Depth Information ◽  
The Neural Network ◽  
Field Information

The light field is an important way to record the spatial information of the target scene. The purpose of this paper is to obtain depth information through the processing of light field information and provide a basis for intelligent medical treatment. In this paper, we first design an attention module to extract the features of light field images and connect all the features as a feature map to generate an attention image. Then, the attention map is integrated with the convolution layer in the neural network in the form of weights to enhance the weight of the subaperture viewpoint, which is more meaningful for depth estimation. Finally, the obtained initial depth results were optimized. The experimental results show that the MSE, PSNR, and SSIM of the depth map obtained by this method are increased by about 13%, 10 dB, and 4%, respectively, in some scenarios with good performance.

scholarly journals Domain Adaptation for Pedestrian Detection Based on Prediction Consistency

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Yu Li-ping ◽  
Tang Huan-ling ◽  
An Zhi-yong
Keyword(s):  
Computer Vision ◽  
Detection Rate ◽  
Domain Adaptation ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Experimental Results ◽  
Challenging Problem ◽  
Target Domain ◽  
Source Domain ◽  
Adaptation Model

Pedestrian detection is an active area of research in computer vision. It remains a quite challenging problem in many applications where many factors cause a mismatch between source dataset used to train the pedestrian detector and samples in the target scene. In this paper, we propose a novel domain adaptation model for merging plentiful source domain samples with scared target domain samples to create a scene-specific pedestrian detector that performs as well as rich target domain simples are present. Our approach combines the boosting-based learning algorithm with an entropy-based transferability, which is derived from the prediction consistency with the source classifications, to selectively choose the samples showing positive transferability in source domains to the target domain. Experimental results show that our approach can improve the detection rate, especially with the insufficient labeled data in target scene.

Application of Face Detection Technology in Automatic Sweep Robot

2013 ◽  
Vol 416-417 ◽  
pp. 1355-1359
Author(s):  
Gu Xiong Li ◽  
Kai Huang
Keyword(s):  
Face Detection ◽  
Image Sensor ◽  
Experimental Results ◽  
Work Efficiency ◽  
Running Speed ◽  
Good Outcome ◽  
Detection Technology

One being developed automatic sweep robot, need to estimate if anyone is on a certain range of road ahead then automatically adjust running speed, in order to ensure work efficiency and operation safety. This paper proposed a method using face detection to predict the data of image sensor. The experimental results show that, the proposed algorithm is practical and reliable, and good outcome have been achieved in the application of instruction robot.

scholarly journals Random Forest with Adaptive Local Template for Pedestrian Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Xiang ◽  
Tao Li ◽  
Mao Ye ◽  
Zijian Liu
Keyword(s):  
Computer Vision ◽  
Random Forest ◽  
Classification Accuracy ◽  
Template Matching ◽  
Detection Method ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Sliding Window ◽  
Experimental Results ◽  
Training Samples

Pedestrian detection with large intraclass variations is still a challenging task in computer vision. In this paper, we propose a novel pedestrian detection method based on Random Forest. Firstly, we generate a few local templates with different sizes and different locations in positive exemplars. Then, the Random Forest is built whose splitting functions are optimized by maximizing class purity of matching the local templates to the training samples, respectively. To improve the classification accuracy, we adopt a boosting-like algorithm to update the weights of the training samples in a layer-wise fashion. During detection, the trained Random Forest will vote the category when a sliding window is input. Our contributions are the splitting functions based on local template matching with adaptive size and location and iteratively weight updating method. We evaluate the proposed method on 2 well-known challenging datasets: TUD pedestrians and INRIA pedestrians. The experimental results demonstrate that our method achieves state-of-the-art or competitive performance.

Technique for Removing Unnecessary Superimposed Patterns from Image using Generative Network

2021 ◽  
Author(s):  
Kazutake Uehira ◽  
Hiroshi Unno
Keyword(s):  
Color Image ◽  
Depth Map ◽  
Experimental Results ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Blue Component ◽  
Component Image

A technique for removing unnecessary patterns from captured images by using a generative network is studied. The patterns, composed of lines and spaces, are superimposed onto a blue component image of RGB color image when the image is captured for the purpose of acquiring a depth map. The superimposed patterns become unnecessary after the depth map is acquired. We tried to remove these unnecessary patterns by using a generative adversarial network (GAN) and an auto encoder (AE). The experimental results show that the patterns can be removed by using a GAN and AE to the point of being invisible. They also show that the performance of GAN is much higher than that of AE and that its PSNR and SSIM were over 45 and about 0.99, respectively. From the results, we demonstrate the effectiveness of the technique with a GAN.

scholarly journals Recognition of Symbolic Gestures Using Depth Information

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Hasan Mahmud ◽  
Md. Kamrul Hasan ◽  
Abdullah-Al-Tariq ◽  
Md. Hasanul Kabir ◽  
M. A. Mottalib
Keyword(s):  
Contextual Information ◽  
Depth Map ◽  
Recognition System ◽  
Support Vector ◽  
Svm Classifier ◽  
Depth Information ◽  
Scale Invariant ◽  
Binary Images ◽  
Depth Images ◽  
Symbolic Gestures

Symbolic gestures are the hand postures with some conventionalized meanings. They are static gestures that one can perform in a very complex environment containing variations in rotation and scale without using voice. The gestures may be produced in different illumination conditions or occluding background scenarios. Any hand gesture recognition system should find enough discriminative features, such as hand-finger contextual information. However, in existing approaches, depth information of hand fingers that represents finger shapes is utilized in limited capacity to extract discriminative features of fingers. Nevertheless, if we consider finger bending information (i.e., a finger that overlaps palm), extracted from depth map, and use them as local features, static gestures varying ever so slightly can become distinguishable. Our work here corroborated this idea and we have generated depth silhouettes with variation in contrast to achieve more discriminative keypoints. This approach, in turn, improved the recognition accuracy up to 96.84%. We have applied Scale-Invariant Feature Transform (SIFT) algorithm which takes the generated depth silhouettes as input and produces robust feature descriptors as output. These features (after converting into unified dimensional feature vectors) are fed into a multiclass Support Vector Machine (SVM) classifier to measure the accuracy. We have tested our results with a standard dataset containing 10 symbolic gesture representing 10 numeric symbols (0-9). After that we have verified and compared our results among depth images, binary images, and images consisting of the hand-finger edge information generated from the same dataset. Our results show higher accuracy while applying SIFT features on depth images. Recognizing numeric symbols accurately performed through hand gestures has a huge impact on different Human-Computer Interaction (HCI) applications including augmented reality, virtual reality, and other fields.

Multi-Resolution-Based Contour Corner Extraction Algorithm for Computer Vision-Based Measurement

2018 ◽  
Author(s):  
Jianhua Li ◽  
Lin Liao
Keyword(s):  
Computer Vision ◽  
Experimental Results ◽  
Original Algorithm ◽  
Industry Standard ◽  
Extraction Algorithm ◽  
Corner Points ◽  
Multiple Resolutions ◽  
Key Steps

Corner-based registration of the industry standard contour and the actual product contour is one of the key steps in industrial computer vision-based measurement. However, existing corner extraction algorithms do not achieve satisfactory results in the extraction of the standard contour and the deformed contour of the actual product. This paper proposes a multi-resolution-based contour corner extraction algorithm for computer vision-based measurement. The algorithm first obtains different corners in multiple resolutions, then sums up the weighted corner values, and finally chooses the corner points with the appropriate corner values as the final contour corners. The experimental results show that the proposed algorithm, based on multi-resolution, outperforms the original algorithm in the aspect of the corner matching situation and helps in subsequent product measurements.

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