Face Detection Problem Research Articles

Real-Time Pre-Identification and Cascaded Detection for Tiny Faces

Although the face detection problem has been studied for decades, searching tiny faces in the whole image is still a challenging task, especially in low-resolution images. Traditional face detection methods are based on hand-crafted features, but the features of tiny faces are different from those of normal-sized faces, and thus the detection robustness cannot be guaranteed. In order to alleviate the problem in existing methods, we propose a pre-identification mechanism and a cascaded detector (PMCD) for tiny-face detection. This pre-identification mechanism can greatly reduce background and other irrelevant information. The cascade detector is designed with two stages of deep convolutional neural network (CNN) to detect tiny faces in a coarse-to-fine manner, i.e., the face-area candidates are pre-identified as region of interest (RoI) based on a real-time pedestrian detector and the pre-identification mechanism, the set of RoI candidates is the input of the second sub-network instead of the whole image. Benefiting from the above mechanism, the second sub-network is designed as a shallow network which can keep high accuracy and real-time performance. The accuracy of PMCD is at least 4% higher than the other state-of-the-art methods on detecting tiny faces, while keeping real-time performance.

Selective Refinement Network for High Performance Face Detection

High performance face detection remains a very challenging problem, especially when there exists many tiny faces. This paper presents a novel single-shot face detector, named Selective Refinement Network (SRN), which introduces novel twostep classification and regression operations selectively into an anchor-based face detector to reduce false positives and improve location accuracy simultaneously. In particular, the SRN consists of two modules: the Selective Two-step Classification (STC) module and the Selective Two-step Regression (STR) module. The STC aims to filter out most simple negative anchors from low level detection layers to reduce the search space for the subsequent classifier, while the STR is designed to coarsely adjust the locations and sizes of anchors from high level detection layers to provide better initialization for the subsequent regressor. Moreover, we design a Receptive Field Enhancement (RFE) block to provide more diverse receptive field, which helps to better capture faces in some extreme poses. As a consequence, the proposed SRN detector achieves state-of-the-art performance on all the widely used face detection benchmarks, including AFW, PASCAL face, FDDB, and WIDER FACE datasets. Codes will be released to facilitate further studies on the face detection problem.

Face detection based on occlusion area detection and recovery

Face detection is an important part of face image processing. In many cases, face images have occlusion problems. In this paper, the POOA (positioning the optimal occlusion area) algorithm is proposed for the problem of occlusion face detection. After obtaining the data of the saliency detection processing, firstly, the algorithm computes an average gray value according to the face image, and multiplies the appropriate coefficient as a threshold to obtain a binary image. Then, using the idea of the Haar feature, the two features of “large rectangle” and “large T shape” are used for retrieval, and the occlusion region of the face is obtained by combining the binary images. Finally, a robust principal component analysis (PCA) method is used to obtain the best projection of the occlusion face, and the face occlusion area is filled. The algorithm proposed in this paper is fast. The Adaboost method has achieved good results in terms of occlusion area, size and shape, and the detection precision has also been improved to varying degrees.

A Novel Face Detection Method Based on Over-complete Incoherent Dictionary Learning

In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the proposed method involves the learning of comprehensive models with atoms that have the highest atom/data coherence with the training data and the lowest within-class and between-class coherence parameters. Each of these goals can be achieved through the proposed procedures. In order to achieve the desired results, a variety of features are extracted from the images and used to learn the characteristics of face and non-face images. Also, the results of the proposed classifier based on the incoherent dictionary learning technique are compared with the results obtained from the other common classifiers such as neural network and support vector machine. Simulation results, along with a significance statistical test show that the proposed method based on the incoherent models learned by the combinational features is able to detect the face regions with high accuracy rate.

Automated Face Detection, Recognition and Gender Estimation Applied to Person Identification

Face detection, recognition and gender estimation are one of the most significant research areas in computer vision, not only because of the challenging nature of faces as an object but also due to the countless applications that require the application of face detection, tracking and recognition. Although many significant types of research on face detection, recognition and gender estimation problems have done in the last few years separately, there is no particular research on face detection, recognition and gender estimation together from a real-time video for person identification. So, we feel that these types of significant research are still needed to work. The main contributions of our paper are divided into three parts, namely face detection, recognition and gender estimation for person identification. In our research work, we use Local Binary pattern Histogram (LBPH) method and Convolution Neural Network (CNN) to extract the facial features of face images whose computational complexity is very low. By calculating the Local Binary Patterns Histogram (LBPH) neighborhood pixels and Convolution levels, we extract effective facial feature to realize face recognition and gender estimation. We show the experimental results using these methods to recognize face and gender for person identification. CNN increase the calculating speed of testing real-time video and also improve the recognition rate. By using LPBH, we get 63% accuracy on average where CNN gives 99.88% training accuracy for face recognition-1 and 96.88% accurate for gender estimation-1 and 100% training accuracy for face recognition-2 and 93.38% training accuracy for gender estimation-2. However, Convolution Neural Networks (CNN) learns fast and predict efficiently.

Single Shot Multi-Face Detection & Gender Recognition

The method is proposed single shot face detection and gender recognition using Convolutional Neural Network(CNN). The proposed method is using the YOLO algorithm to detect the human face and make gender recognition. The face detection and gender recognition are very interesting since the past two decades. This can be used in future for the security purpose, biometric, digital cosmetic and many more. As human face is a dynamic object having a high degree of variability in its appearance, that make the face detection problem difficult in the computer vision task. The goal of this paper is to multiple face detection and its gender recognition in one shot of image is passed in the network and give the better performance in term of speed and accuracy.

Face alignment by Component Adaptive Mechanism

The new cascaded shape regression architecture proposed in this paper is actually an algorithm by Component Adaptive Mechanism (CAM) to cope with unconstrained face alignment. CAM divides the process of face alignment into two parts: the updating process of face box and the cascaded shape regression process by Component Adaptive Mechanism. The former process first adjusts face box by training different classifiers to estimate its transformation parameters and thereby outputs more accurate initialized shape. The latter process uses Component Adaptive Mechanism to fuse results of different domain-specific regressors to further update the shape. The major innovation of CAM is characterized by its fault-tolerated mechanism which is shown in the following two aspects. (1) A probability-based fern classifier is adopted in the partition of the optimization space into multiple domains of homogeneous descent, which not only endows the algorithm with the fault-tolerance mechanism but also augments the available training set of each domain. (2) A training strategy based on dominant set approach is used to train a stronger domain-specific regressor by dynamically adjusting the weight of objective function corresponding to different shapes and therefore regressors derived from the training are equipped with fault-tolerance ability. Conducted on such public image datasets as AFLW-full (19-pts), COFW (29-pts) and 300-W (68-pts), experiments show that the proposed CAM: (1) can deal with the problem of face detection and face alignment simultaneously; (2) is superior to existing algorithms in solving face alignment problems with extreme variations in pose, expression, illumination and partial occlusion.

BAdaCost: Multi-class Boosting with Costs

We present BAdaCost, a multi-class cost-sensitive classification algorithm. It combines a set of cost-sensitive multi-class weak learners to obtain a strong classification rule within the Boosting framework. To derive the algorithm we introduce CMEL, a Cost-sensitive Multi-class Exponential Loss that generalizes the losses optimized in various classification algorithms such as AdaBoost, SAMME, Cost-sensitive AdaBoost and PIBoost. Hence unifying them under a common theoretical framework. In the experiments performed we prove that BAdaCost achieves significant gains in performance when compared to previous multi-class cost-sensitive approaches. The advantages of the proposed algorithm in asymmetric multi-class classification are also evaluated in practical multi-view face and car detection problems.

RETRACTED ARTICLE: Video Face Detection Based on Deep Learning

In recent years, with the development of Internet plus concept, online identity has become a major problem based on the continuous expansion of network applications. The online authentication technology based on biometric features can maintain the consistency of human digital identity and physical identity, so people pay more attention to it. This paper studies the problem of human face detection. The main tasks are as follows: an active body detection algorithm for convolution neural networks based on dynamic feature is proposed. First, the Pyramid LK optical flow method is used to track the video, and the dynamic information of the image is obtained. Then, the information of the optical flow is analysed, and the horizontal and vertical displacement are calculated. According to the two displacements, the displacement amplitude diagram is calculated, that is, the dynamic feature graph. The dynamic feature graph is used as the input of the convolution neural network. Finally, the feature extraction and the living detection are carried out. A face authentication system with living face detection function is designed. The system includes the registration phase and the authentication phase. The registration phase includes face image detection and feature extraction module. The authentication phase includes face detection, human face discrimination, feature extraction and similarity calculation module.

Rapid Human Finding with Motion Segmentation for Mobile Robot

A computer vision method is presented for the mobile robot to find humans in scene. Face detection is used for confirming humans. In order to reduce regions of search, optical flow algorithm is used to segment the image in advance. Asymmetric problems in face detection are explained, and relative solutions are put forward by bootstrapping strategy and asymmetric adaboost algorithm. In addition, fisher discriminant analysis further improves the performance of face detection. Multi-view face models are trained to accommodate practical face detection application. At last, experiments demonstrate that our multi-view face detector achieves high detection accuracy and fast detection speed on both standard testing datasets and real-life images.

Efficient 3D point clouds classification for face detection using linear programming and data mining

ABSTRACTMost of the applications related to security and biometric rely on skin region detection such as face detection, adult 3D objects filtering, and gesture recognition. In this paper, we propose a robust method for skin detection on 3D coloured point clouds. Then, we extend this method to solve the problem of 3D face detection. To do so, we construct a weighted graph from initial coloured 3D point clouds. Then, we present a linear programming algorithm using a predictive model based on a data mining approach to classify and label graph vertices as skin and non-skin regions. Moreover, we apply some refinement rules on skin regions to confirm the presence of a face. Furthermore, we demonstrate the robustness of our method by showing and analysing some experimental results. Finally, we show that our method deals with many data that can be represented by a weighted graph such as 2D images and 3D models.

Face Detection Based on Skin Color and Adaboost Algorithm

The problem of multiple face detection and missed detection caused by illumination and complex background is avoided. A multi face detection algorithm based on skin color and Adaboost algorithm is proposed. The first chapter of this article is to apply the CbCrCg space based on the least squares of the skin color clustering model as the pretreatment process of face detection, And then you need to go further and take the area as an input image and you go through the Adaboost cascade classifier for face detection, To further remove non-face areas in the skin area, Finally, a more accurate face orientation is achieved.

The application of an interactively recurrent self-evolving fuzzy CMAC classifier on face detection in color images

This study proposes an interactively recurrent self-evolving fuzzy cerebellar model articulation controller (IRSFCMAC) classifier to solve face detection problems. The learning methods of the proposed classifier are based on simultaneous structure and parameter learning. The structure learning is used to decide the proper input space partition, while the parameter learning is based on gradient descent method. The online structure learning does not need to set any initial structure in advance. In other words, the online structure learning algorithm enables the network along of the problem to efficiently identify the required network structure. The advantages of our proposed IRSFCMAC classifier include (1) using a non-constant differentiable Gaussian basis function to model the hypercube structure; (2) applying an interactively recurrent structure to serve as external loops and internal feedbacks by feeding the hypercube cell (rule) firing strength to itself and other hypercube cells (rules); and (3) requiring fewer computing memory. Finally, experimental results show that the proposed IRSFCMAC classifier is a more adaptive and effective face detection than the other classifiers.

Face Detection Using Fusion of LBP and AdaBoost

The human face is a dynamic object which a high degree of variability exists in its appearance. This makes the face detection to be considered as the first and most essential step in face recognition systems. The aim of the face detection is the segmentation of the face. There are several problems in face detection, including state switching, changing light intensity, face covering, etc. To solve these problems, the present study utilizes an advanced algorithm based on the LBP method and a structured Adaboost algorithm using the FERET database. The output will be attained by entering the normalized data into a three-layer LBP Adaboost fed with the data from data bases. It was found that the method has the acceptable detection accuracy of 75%, detection feature of 72% and sensitivity of 77% , and the final detection result of the human face is up to 98% possible.

Empirical analysis of cascade deformable models for multi-view face detection

We present a multi-view face detector based on Cascade Deformable Part Models (CDPM). Over the last decade, there have been several attempts to extend the well-established Viola&Jones face detector algorithm to solve the problem of multi-view face detection. Recently a tree structure model for multi-view face detection was proposed. This method is primarily designed for facial landmark detection and consequently a face detection is provided. However, the effort to model inner facial structures by using a detailed facial landmark labelling resulted on a complex and suboptimal system for face detection. Instead, we adopt CDPMs, where the models are learned from partially labelled images using Latent Support Vector Machines (LSVM). Furthermore, LSVM is enhanced with data-mining and bootstrapping procedures to enrich models during the training. Furthermore, a post-optimization procedure is derived to improve the performance. This semi-supervised methodology allows us to build models based on weakly labelled data while incrementally learning latent positive and negative samples. Our results show that the proposed model can deal with highly expressive and partially occluded faces while outperforming the state-of-the-art face detectors by a large margin on challenging benchmarks such as the Face Detection Data Set and Benchmark (FDDB) [1] and the Annotated Facial Landmarks in the Wild (AFLW) [2] databases. In addition, we validate the accuracy of our models under large head pose variation and facial occlusions in the Head Pose Image Database (HPID) [3] and Caltech Occluded Faces in the Wild (COFW) datasets [4], respectively. We also outline the suitability of our models to support facial landmark detection algorithms.

A Study on Components and Features in Face Detection

It's natural and convenient that the face is used as a feature in the city safety monitoring. Components and feature extraction are two key problems in face detection. To address the local occlusion and pose variation in face detection, face can be looked on as a whole composed of several parts from up to down. First, the face is divided into a number of local regions from which various features are extracted. Each region is identified by a local classifier and is assigned a preliminary part label. A random field is established based on these labels and multiple dependencies between different parts are modeled in a CRF framework. The probability that the test image may be a face is calculated by a trained CRF model. The probability is used as a measure to test the existence of a face. The experiments were carried out on the CMU/MIT dataset. As indicated by the experiment results, the following methods can improve the detection rate and enhance the robustness of face detection in case of occlusion: 1) integrating multiple features and multiple dependencies in CRF framework; 2) dividing the face optimally.

A Face Detection Method Used for Color Images

To the problem of face detection in color images, a novel method is proposed based on skin color segmentation and geometry features. Firstly, some common color models are analyzed, and a large amount of skin images are used to establish an YCbCr color model for region segmentation. Then, the morphological processing is executed on the binary image, and the facial regions filtering is conducted by adopting some geometry constraints such as Euler number, the ratio of width and height, centroid. Finally, the face region is located and labeled with a rectangle. Experimental results showed that the proposed method can equilibrate the unbalance between good accuracy and real-time performance to some extent, and is effective in face location.

ЭФФЕКТИВНАЯ ДЕТЕКЦИЯ ЛИЦ НА МНОГОЯДЕРНОМ ПРОЦЕССОРЕ EPIPHANY

It is studied the possibility of usage of energy-efficient Epiphany microprocessor for solving actual applied problem of face detection at still image. The microprocessor is a multicore system with distributed memory, implemented in a single chip. Due to small die area the microprocessor has significant hardware limitations (in particular it has only 32 kilobytes of memory per core) which limit the range of usable algorithms and complicate their software implementation. Common face-detection algorithm based on local binary patterns (LBP) and cascading classifier was adapted for parallel implementation. It is shown that Epiphany microprocessor having 16 cores can outperform single-core CPU of personal computer having the same clock rate by a factor of 2.5, while consuming only 0.5 watts of electric power.

Multi-View Face Detection Based on Multi-Features AdaBoost Collaborative Learning Algorithm

In View of the Multi-View Face Detection Problem under Complex Background, an Improved Face Detection Method Based on Multi-Features Boosting Collaborative Learning Algorithm Integrating Local Binary Pattern (LBP) is Presented. Firstly, Facial Skin Color Information is Used to Exclude most of the Background Regions. then, Haar-like Feature and LBP Feature are Extracted from Facial Candidate Regions and Inputted into a Modified Adaboost Algorithm to Obtain a Strong Classifier. Lastly, in Order to Improve the Detection Speed, Pyramid Classifier System Structure is Adopted to Determine the Face. the Experimental Results on CMU Standard Test Set and Life Photos, the Proposed Method has Achieved the Rapid Detection of Multi-View Face Image.

Face Detection using Color based Segmentation and Edge Detection

The increasing use of computer vision in security in place of humans led many to research the problem of face detection in images. The problem is not a petty one as the classification of a human face proves to challenging. Despite the many variations of a human face, features can still be found, given a certain context, which will uniquely identify a face. Early face-detection algorithms focused on the detection of frontal human faces, whereas this paper attempt to solve the more general and difficult problem of multi-view face detection. Face detection involves many research challenges such as scale, rotation, and pose and illumination variation. The techniques used for face detection have been researched for years and much progress has been suggested in literature. This paper proposes a new technique for detecting faces in color images using color model and edge detection. Face detection is used in as a part of a facial recognition system. It is also used in human computer interface, image database management and video surveillance. The results of this technique show that the proposed algorithm is good enough to detect the human face taken through video with accuracy. This paper is achieving high detection speed, high detection accuracy and reduces the false detecting rate.