Face Detection Module Research Articles

Matching images captured from unmanned aerial vehicle

Police database cannot have images of first-time offenders; hence, apprehending them becomes a very challenging task. In this paper, we propose a novel technique to apprehend first-time offenders using composite sketches and images captured by unmanned aerial vehicles. The key contribution of this paper is we have developed a new technology to match composite sketches with images captured by unmanned aerial vehicle to apprehend first-time criminals in a very short time period. The unmanned aerial vehicle is sent in the area where the first-time offender is likely to be present. The image captured by unmanned aerial vehicle is passed to face detection module so that only human faces are obtained. Feature extraction is performed using multi-resolution uniform local binary pattern, and classification is performed using dictionary matching. This proposed method is validated by composite sketches generated using SketchCop FACETTE face design system software and images captured by Phantom 3 professional unmanned aerial vehicle.

Developing a Novel Technique to Match Composite Sketches with Images Captured by Unmanned Aerial Vehicle

Police database cannot have images of first time offenders hence apprehending them becomes a difficult task. In this paper we propose a new method which matches composite sketches with images captured by Unmanned Aerial Vehicles (UAV). The UAV is sent in the area where the offender is likely to be present. The human face, obtained by passing the captured image to Face Detection module, is subjected to Image Quality Assessment (IQA) calculating module. Based on the IQA values obtained we decide if the given face image contains Pose variations, Illumination variations, Age variation etc. Based on the variations, suitable feature extraction and classification method is selected.

Real-Time Computer Vision System for Continuous Face Detection and Tracking

The ever-increasing number of traffic accidents due to a diminished driver’s vigilance level has become a problem of serious concern to society. With the ever growing traffic conditions, this problem will further deteriorate. For this issue, development of system which can actively monitors driver vigilance level and alert the driver for any insecure driving condition is essential. So this paper gives detailed information about driver vigilance level monitoring system. The ultimate goal of the system is to detect and alert the driver from insecure sleepy or low concentration driving condition. The system consists of two main modules including drivers face and eye detection module and drivers face tracking module. Viola Jones face detection with AdaBoost (AdaptiveBoosting) method and Circular Hough Transform technique are integrated in the drivers face and eye detection module. In the drivers face tracking module, CAMSHIFT (Continuously Adaptive Mean Shift) algorithm has been used for continuous face tracking of driver. The main components of the system consist of a video camera, a specially designed hardware system based on Raspberry Pi for real-time image processing and controlling the alarm system. In the proposed system, only one video camera is used in practice yet an achievement of fast and accurate detection results are obtained.

Research of AdaBoost Face Detection and OpenCV Implementation

We introduce the principle of AdaBoost face detection algorithm based on Haar-like feature in detail, and use OpenCV face detection module to realize AdaBoost algorithm on CMU face library. Experimental results show that the algorithm receives good effects. Meanwhile we analyze the relationship between the number of simple classifier and face detection result.

Face region authentication and recovery system based on SPIHT and watermarking

The face regions of digital pictures are some of the principal target of tampering to generate a potential scandal, causing social and economic damages to involved persons. In this paper, we propose a face region authentication and recovery system, in which the face regions are automatically protected at the moment when the picture is taken by a digital camera. When the original face of the picture is replaced by another face by malicious person, the system can detect the tampered face and recover the original one. The proposed system consists of two stages: the face region protection stage and the face region tamper detection and recovery stage. In both stages, the face detection module based on the Viola-Jones algorithm, face region encoding/decoding modules based on the Set Partitioning in Hierarchical Trees (SPIHT) algorithm and watermarking module based on Quantization Index Modulation (QIM) are used. These three algorithms, Viola-Jones detector, SPIHT and QIM, are determined as most adequate algorithms for proposed system after several evaluations. The experimental results show a high quality of the watermarked as well as the recovered images, obtaining average Peak Signals to Noise Ratios (PSNR) of more than 40 and 38 dB, respectively.

Enhanced long-range personal identification based on multimodal information of human features

This work presents an enhanced long-range personal identification scheme using multimodal information of human features. Multimodal information includes multiview face detection, height measurement and face recognition. Multiview faces are estimated by collecting five face databases that correspond to left, half-left, right, half-right, and frontal faces, respectively. The sequences of parameters based on four detectors are also designed to determine the face direction. The detectors use the head-shoulder region, frontal face, profile face, and eyes detector respectively. In addition to determining when individuals enter the monitoring area, the multiview face detection module also describes the detected face direction. This result allows the identification system to select the face database of a specific direction to identify subsequent faces. Additionally, the height measurement module estimates individual height by calculating the vanishing points and lines. The module concept is based on single-view metrology. The measured information further refines the face database selected by multiview face detection and minimizes the candidates for face identification. Importantly, the proposed method integrates the multimodal information based on face direction, height and face features to refine the database and analyzes the information to determine the identity of a person. In this work, images from a monitoring area 5.6 m away from a camera are captured using an inexpensive digital web camera. The experimental results show that the proposed method can improve the accuracy rate by more than 21 % in contrast with the baselines and correspondingly demonstrates the effectiveness of the proposed idea.

실시간 응용을 위한 안드로이드 플랫폼에서의 안면 검출 시스템 구현

This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.

ANALYSIS OF HUMAN FACE RECOGNITION ALGORITHM USING PCA+FDIT IN IMAGE DATABASE FOR CRIME INVESTIGATION

In general, the field of face recognition has lots of research that have put interest in order to detect the face and to identify it and also to track it. Many researchers have concentrated on the face identification and detection problem by using various approaches. The proposed approach is further very useful and helpful in real time application. Thus the Face Detection, Identification  which is proposed here is used to detect the faces in videos in the real time application by using the FDIT (Face Detection Identification Technique) algorithm. Thus the proposed mechanism is very help full in identifying individual persons who are been involved in the action of robbery, murder cases and terror activities. Although in face recognition the algorithm used is of histogram equalization combined with Back propagation neural network in which we recognize an unknown test image by comparing it with the known training set images that are been stored in the database. Also the proposed approach uses skin color extraction as a parameter for face detection. A multi linear training and rectangular face feature extraction are done for training, identifying and detecting.   Thus the proposed technique   is PCA + FDIT technique configuration only improved recognition for subjects in images are included in the training data.  It is very useful in identify a single person from a group of faces.   Thus the proposed technique is well suited for all kinds faces frame work for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier.  Also we have taken a real life example and simulated the algorithms in IDL Tool successfully.

AUTOPARK: A Sensor Based, Automated, Secure and Efficient Parking Guidance System

Nowadays, due to modern and lavish lifestyles people prefer their own vehicles while going anywhere, resulting in increased traffic congestion and parking problems. In this paper, we propose and present a completely automated system AUTOPARK that will solve the parking issues without any human intervention. The system will comprise of an infrastructure based wired sensor network with sensors placed at each parking slot to detect the occupancy of the parking slots. It also comprises of an automated voice enabled system for guiding vehicles to the available vacant slots by giving directions. The system also enables security and takes into consideration the prevention of theft of vehicles, for this purpose we incorporate face detection and number plate recognition modules so that a track record can be maintained. In case of any mishap or unidentified car or person, communication can be made with base station for immediate human assistance. The paper further discusses design model, the architectural layout and methodology used to build AUTOPARK. It concludes with system evaluation and efficiency analysis of the system. Keywords - Face Detection, Number plate recognition, Pressure sensor, Text-to-Speech, Ultrasonic Sensor

Unified Framework for Automated Iris Segmentation Using Distantly Acquired Face Images

Remote human identification using iris biometrics has high civilian and surveillance applications and its success requires the development of robust segmentation algorithm to automatically extract the iris region. This paper presents a new iris segmentation framework which can robustly segment the iris images acquired using near infrared or visible illumination. The proposed approach exploits multiple higher order local pixel dependencies to robustly classify the eye region pixels into iris or noniris regions. Face and eye detection modules have been incorporated in the unified framework to automatically provide the localized eye region from facial image for iris segmentation. We develop robust postprocessing operations algorithm to effectively mitigate the noisy pixels caused by the misclassification. Experimental results presented in this paper suggest significant improvement in the average segmentation errors over the previously proposed approaches, i.e., 47.5%, 34.1%, and 32.6% on UBIRIS.v2, FRGC, and CASIA.v4 at-a-distance databases, respectively. The usefulness of the proposed approach is also ascertained from recognition experiments on three different publicly available databases.

The Application of Face Recognition Based on OpenCV

Face recognition is a significant method, which is one of the biometric recognition. A face recognition system consists of two key technologies, namely, face detection and face recognition. In order to achieve two key technologies, Haar-Like feature and AdsBoost algorithm can be used to achieve face detection module. And PCA algorithm can be used to achieve face recognition module. For achieve application more quickly and efficiently, the core of the system develops with OpenCV. And the main use is its image processing, mathematical operations, and machine learning functions.

Lip reading of hearing impaired persons using HMM

This paper describes a method for lip reading of hearing impaired persons. The term lip reading refers to recognizing the spoken words using visual speech information such as lip movements. The visual speech video of the hearing impaired person is given as input to the face detection module for detecting the face region. The region of the mouth is determined relative to the face region. The mouth images are used for feature extraction. The features are extracted using discrete cosine transform (DCT) and discrete wavelet transform (DWT). Then, these features are applied separately as inputs to the hidden markov model (HMM) for recognizing the visual speech. To understand the visual speech of hearing impaired person in cash collection counters, 33 words are chosen. For each word, 20 samples are collected for training the HMM model and another five samples are used for testing the model. The experimental results show that the method gives the performance of 91.0% for the DCT based lip features and 97.0% for DWT based lip features.

Computer vision based eyewear selector

The widespread availability of portable computing power and inexpensive digital cameras are opening up new possibilities for retailers in some markets. One example is in optical shops, where a number of systems exist that facilitate eyeglasses selection. These systems are now more necessary as the market is saturated with an increasingly complex array of lenses, frames, coatings, tints, photochromic and polarizing treatments, etc. Research challenges encompass Computer Vision, Multimedia and Human-Computer Interaction. Cost factors are also of importance for widespread product acceptance. This paper describes a low-cost system that allows the user to visualize different glasses models in live video. The user can also move the glasses to adjust its position on the face. The system, which runs at 9.5 frames/s on general-purpose hardware, has a homeostatic module that keeps image parameters controlled. This is achieved by using a camera with motorized zoom, iris, white balance, etc. This feature can be specially useful in environments with changing illumination and shadows, like in an optical shop. The system also includes a face and eye detection module and a glasses management module.

Real-time follow-up head tracking in dynamic complex environments

In the modes of both object motion and camera motion, an enhanced Camshift algorithm, which is based on suppressing similar color features of background and on joint color probability density distribution image, is proposed to real-time track head in dynamic complex environment. The system consists of face detection module, head tracking module and camera control module. When tracking fails, a self-recovery mechanism is introduced. At first the Adaboost face detector based on Haar-like features is implemented to find frontal faces, the false positive is filtered according to the skin color criterion, and the true face is used to initialize the tracking module. In hue saturation value (HSV) colorspace, the hue-saturation (H-S) histogram of face skin and the saturation-value (S-V) histogram of hair are built to produce the joint color probability density distribution image, and this is intended to realize the head tracking with arbitrary pose. During tracking, region of interest (ROI) is introduced, and the color probability density distribution of a specified background area outside the ROI is learned, similar color features in the head are suppressed according to the learning result. The background suppression step is intended to resolve the problem that the tracker maybe fails when the head is distracted by backgrounds having similar colors with the head. A closed loop control model based on speed regulation is applied to drive an active camera to center the head. Once tracking drift or failure is detected, the system stops tracking and returns to the face detection module. Our experimental results show that the presented system is well suitable for tracking head with arbitrary pose in dynamic complex environments, also the active camera can track moving head smoothly and stably. The system is computationally efficient and can run in real-time completely.

Multimodal Approach to Human-Face Detection and Tracking

The constructive need for robots to coexist with humans requires human-machine interaction. It is a challenge to operate these robots in such dynamic environments, which requires continuous decision-making and environment-attribute update in real-time. An autonomous robot guide is well suitable in places such as museums, libraries, schools, hospital, etc. This paper addresses a scenario where a robot tracks and follows a human. A neural network is utilized to learn the skin and nonskin colors. The skin-color probability map is utilized for skin classification and morphology-based preprocessing. Heuristic rule is used for face-ratio analysis and Bayesian cost analysis for label classification. A face-detection module, based on a 2D color model in the and YUV color space, is selected over the traditional skin-color model in a 3D color space. A modified continuously adaptive mean shift tracking mechanism in a 1D hue, saturation, and value color space is developed and implemented onto the mobile robot. In addition to the visual cues, the tracking process considers 16 sonar scan and tactile sensor readings from the robot to generate a robust measure of the person's distance from the robot. The robot thus decides an appropriate action, namely, to follow the human subject and perform obstacle avoidance. The proposed approach is orientation invariant under varying lighting conditions and invariant to natural transformations such as translation, rotation, and scaling. Such a multimodal solution is effective for face detection and tracking.

A New Anchor Shot Detection System for News Video Indexing

In this paper, we propose a novel anchor shot detection system, named to MASD (Multi-phase Anchor Shot Detection), which is a core step of the preprocessing process for the news video analysis. The proposed system is composed of four modules and operates sequentially: 1) skin color detection module for reducing the candidate face regions; 2) face detection module for finding the key-frames with a facial data; 3) vector representation module for the key-frame images using a non-negative matrix factorization; 4) one class SVM module for determining the anchor shots using a support vector data description. Besides the qualitative analysis, our experiments validate that the proposed system shows not only the comparable accuracy to the recently developed methods, but also more faster detection rate than those of others.

A Component-based Framework for Face Detection and Identification

We present a component-based framework for face detection and identification. The face detection and identification modules share the same hierarchical architecture. They both consist of two layers of classifiers, a layer with a set of component classifiers and a layer with a single combination classifier. The component classifiers independently detect/identify facial parts in the image. Their outputs are passed the combination classifier which performs the final detection/identification of the face. We describe an algorithm which automatically learns two separate sets of facial components for the detection and identification tasks. In experiments we compare the detection and identification systems to standard global approaches. The experimental results clearly show that our component-based approach is superior to global approaches.

A facial expression recognition system based on supervised locally linear embedding

In this paper, a facial expression recognition system based on supervised locally linear embedding (SLLE) is introduced. The system consists of three modules: face detection, feature extraction with SLLE and classification. In face detection module, two independent characteristics, skin color characteristic and motion characteristic are used to detect face region, and a trained SVM is used to verify candidate regions. In feature extraction module, SLLE, a supervised learning algorithm that can compute low dimensional, neighborhood-preserving embeddings of high dimensional data is used to reduce data dimension and extract features. In classification module, minimum-distance classifier is used to recognize different expressions. The experiments show that the proposed method is superior to PCA-based method.

IJADE surveillant—an intelligent multi-resolution composite neuro-oscillatory agent-based surveillance system

Due to the rapid development of technology, especially in the field of Internet systems, there is an increasing demand both for intelligent, mobile and autonomous systems and for the usage and conveyance of multi-media information through cyberspace. In this paper, we propose an innovative, intelligent multi-agent based model, namely intelligent Java Agent Development Environment (iJADE), to provide an intelligent agent-based platform in the e-commerce environment. In addition to the facilities found in contemporary agent development platforms, which focus on the autonomy and mobility of multi-agents, iJADE provides an intelligent layer (known as the “Conscious Layer”) to support the implementation of various AI functionalities in order to produce “smart” agents. From an implementation point of view, we introduce an intelligent, multi-media processing system known as “ iJADE Surveillant”—an intelligent multi-resolution composite neuro-oscillatory agent-based surveillance system—which is based on the integration of the following modules. (a) An automatic coarse-to-fine figure-ground scene segmentation module using the Composite Neuro-Oscillatory Wavelet-based model. (b) An automatic human face detection and extraction module using an Active Contour Model with facial “landmarks” vectors. (c) Invariant human face identification based on the Elastic Graph Dynamic Link Model. To conform to the current (and future) multi-media system standards, all of iJADE Surveillant is implemented using the MPEG-7 system framework—with comprehensive Description Schemes, feature descriptors and a model framework. From an experimental point of view, a scene gallery of over 6000 color scene images is used to test the automatic scene segmentation. One hundred distinct human subjects (with over 1020 tested scenes) are used to test the intelligent human face identification. An overall correct (invariant) facial recognition rate of over 90% is attained. We hope that the implementation of the iJADE Surveillant can provide an invariant and higher-order intelligent object (pattern) encoding, searching and identification solution for future MPEG-7 applications.

Towards invariant face recognition

A face recognition method that can cope with the variations due to the changes in lighting and pose is presented in this paper. This system uses the output of a face detection module that extracts human faces within an input scene image. To compensate for illumination effects, an algorithm is proposed which applies an embossing operator to the detected face images to remove the illumination effects. Next, variations in the face images due to changes in pose are compensated for by using an algorithm that generates corresponding front-view face images of the detected face images; thus the perspective invariant recognition of the input face image becomes feasible. This algorithm first calculates a 3D head model from a 2D input face image. Once a 3D head is found, 2D face images can be rendered under different pose and lighting effects. For face recognition, a front-view face image is rendered. The resulting front-view face image is then directed to a fractal-based recognition algorithm. Three different experiments are carried out on a large collection of face images. The results are presented and discussed.