Face Detection Dataset And Benchmark Research Articles

EEFDet: an efficient and effective face detector for lightweight, fast, and accurate face detection

In practical applications, it is urgent to develop lightweight, fast, and accurate face detectors. Although the performance of face detection has made tremendous progress with the rapid development of deep learning, they still struggle to meet the requirements of both efficiency and effectiveness. This is because current face detectors either use large networks or cumbersome processes oriented for accuracy but leading to heavy model complexity and slow inference speed or use various simple designs oriented for efficiency but greatly sacrificing accuracy or not actually efficient. In this paper, we conduct an in-depth study of the network structure and anchor setting and present an efficient and effective face detector named EEFDet, which can detect faces with lightweight model, fast inference speed, and excellent accuracy. First, a lightweight, fast, and robust network (LFRNet) is constructed by carefully designing the backbone network, detection neck, and detection head of the network. LFRNet not only has small model complexity and low latency but also possesses robust feature extraction ability. Then, a sparse anchor strategy is proposed by rationally setting anchors associated with the detection head to avoid using too many redundant anchors and get the most effective anchors. This strategy enables the detector to process multiple anchor-related parts at a fast speed while still guaranteeing detection accuracy. Extensive experiments and detailed ablation analysis are conducted on several widely used face detection benchmarks based on multiple metrics and different platforms. The results show that our EEFDet is lightweight (1.195 M parameters and 0.719 G FLOPs), fast (38.780 ms on GPU and 141.787 ms on CPU), and accurate [90.8%/89.6% (easy), 88.1%/87.2% (medium), 77.9%/77.4% (hard) APs on Val/Test set of WIDER FACE, 96.3% discrete score and 79.7% continuous score on face detection data set and benchmark (FDDB), 97.7% AP on PASCAL FACE, and 98.2% AP on annotated face in-the-wild (AFW)], achieving an optimal trade-off between efficiency and effectiveness compared to state-of-the-art face detectors.

An Efficient Multi-Scale Anchor Box Approach to Detect Partial Faces from a Video Sequence

In recent years, face detection has achieved considerable attention in the field of computer vision using traditional machine learning techniques and deep learning techniques. Deep learning is used to build the most recent and powerful face detection algorithms. However, partial face detection still remains to achieve remarkable performance. Partial faces are occluded due to hair, hat, glasses, hands, mobile phones, and side-angle-captured images. Fewer facial features can be identified from such images. In this paper, we present a deep convolutional neural network face detection method using the anchor boxes section strategy. We limited the number of anchor boxes and scales and chose only relevant to the face shape. The proposed model was trained and tested on a popular and challenging face detection benchmark dataset, i.e., Face Detection Dataset and Benchmark (FDDB), and can also detect partially covered faces with better accuracy and precision. Extensive experiments were performed, with evaluation metrics including accuracy, precision, recall, F1 score, inference time, and FPS. The results show that the proposed model is able to detect the face in the image, including occluded features, more precisely than other state-of-the-art approaches, achieving 94.8% accuracy and 98.7% precision on the FDDB dataset at 21 frames per second (FPS).

Analysis and fast feature selection technique for real-time face detection materials using modified region optimized convolutional neural network

For face-related applications like face recognition and face verification, face detection is vital. Law enforcement agencies extensively use face recognition applications for the detection of known offenders. Security cameras are increasingly deployed everywhere, and these cameras’ captured data can be used for tagging offenders. This is done by offering the face detection system with input images, which are then processed. Later on, attempts will be done to tag the security cameras’ captured data with this processed input data. Effective utilization of deep learning techniques in order to efficiently carry out this task. Computer vision’s various tasks have been taken over by deep Convolutional Neural Networks (CNNs). Of late, the key paradigms for face detection are the region-based CNN (R-CNN) detection models due to their quicker processing speed and progressively better performance. Nevertheless, the huge feature set causes these models’ to be inefficient for real-time face detection. A Grammatical Evolution (GE) Region-Based Fully Conventional Neural Network (R-FCN) for face detection has been proposed in this work in which the GE heuristics are utilized for tuning the hyper-parameters. The WIDER face dataset and Face Detection Dataset and Benchmark (FDDB) dataset were employed for conducting the assessments. In comparison to other advanced paradigms, the proposed technique’s superiority is evident from the experiments conducted.

SINGLE-SHOT FACE DETECTION AND RECOGNITION BASED ON CNN

The paper proposes an architecture and training method of a deep convolutional neural network for simultaneous face detection and recognition. The implemented approach combines the ideas of SSD (Single Shot Detector) and Faster R-CNN (Region proposal Convolutional Neural Networks) algorithms. Face detection is performed similarly to single-stage detection algorithms, and then a biometric template is built by employing RoI (Region of Interest) pooling layers and using the separate branch of the neural network. Training process includes three stages: pretraining of thebasic CNN for face recognition on face images, fine-tuning by using RoI pooling on in painted face images, adding SSD layers and fine-tuning on face detection. Wherein, at the latter stage, training is performed by using shared layers technology for two databases simultaneously. The main feature of the algorithm is high processing speed, which does not depend on the number of faces in the input image. For example, in case of using ResNet-34 as the core architecture for the algorithm, the required time for detecting faces and building biometric templates on an image with 100 faces is less than 13 ms. For training purposes we use CASIA-WebFace for face recognition task and Wider Face for face detection task. Testing is performed on FDDB (Face Detection Dataset and Benchmark), since this database is closer to practical applications than Wider. As long as the main practical task the developed method is intended for is face reidentification, we use Fei Face DataBase for face recognition quality testing. We obtain TPR (True Positive Rate) = 0.928@1000 on FDDB Face DataBase and FAR (Face Acceptance Rate) = 0.03309@FRR (Face Rejection Rate) = 10–4. Therefore, the proposed algorithm allows solving face detection and reidentification tasks in real time with any number of faces on an input image.

End-to-End Spatial Transform Face Detection and Recognition

BackgroundSeveral face detection and recognition methods have been proposed in the past decades that have excellent performance. The conventional face recognition pipeline comprises the following: (1) face detection, (2) face alignment, (3) feature extraction, and (4) similarity, which are independent of each other. The separate facial analysis stages lead to redundant model calculations, and are difficult for use in end-to-end training. MethodsIn this paper, we propose a novel end-to-end trainable convolutional network framework for face detection and recognition, in which a geometric transformation matrix is directly learned to align the faces rather than predicting the facial landmarks. In the training stage, our single CNN model is supervised only by face bounding boxes and personal identities, which are publicly available from WIDER FACE and CASIA-WebFace datasets. Our model is tested on Face Detection Dataset and Benchmark (FDDB) and Labeled Face in the Wild (LFW) datasets. ResultsThe results show 89.24% recall for face detection tasks and 98.63% accuracy for face recognition tasks.

DEFace: Deep Efficient Face Network for Small Scale Variations

This study proposes a novel face detector called DEFace that focuses on the challenging tasks of face detection to cope with a small size that is under 12 pixels and occlusions due to a mask or human body parts. This study proposed the extended feature pyramid network (FPN) module to detect small faces by expanding the range of P layer, and the network by adding a receptive context module (RCM) after each predicted feature head from the top-down pathway in the FPN architecture to enhance the feature discriminability and the robustness. Based on the FPN principle, the combination between the low- and high-resolutions are beneficial for object detection with different object sizes. Furthermore, with assistance from the RCM, the proposed method can use a broad range of context information especially for small faces. To evaluate the performance of the proposed method, various public face datasets are used such as the WIDER Face dataset, the face detection dataset and benchmark (FDDB), and the masked faces (MAFA) dataset, which consist of challenging samples such as small face regions and occlusions by hair or other people. The results indicate that DEFace can detect the face region more accurately in comparison to the other state-of-the-art methods while maintaining the processing time.

A New Facial Detection Model based on the Faster R-CNN

The object detection approaches in conjunction with Fast/Faster R-CNN and YOLO have shown the benchmarking performance on several occasions. Inspired by the Refine Net, we propose a new model called Faster+ R-CNN based on Faster R-CNN, which is mainly based on iterative refinement on the proposed regions. The Faster+ R-CNN model can iteratively refine the region proposal based on previous output. We trained and tested our new model on PASCAL VOC 2007 dataset, and experiments showed that our method can iteratively improve the mean average precision (mAP) from 0.6702 to 0.6764 in object detecting task. We also demonstrate the facial detection results using the Faster+ R-CNN on the widely used Face Detection Dataset and Benchmark (FDDB) benchmark. By training the Faster+ R-CNN model on the large scale WIDER face dataset, we report the improved results on two widely used face detection benchmarks including FDDB.

A Fast and Lightweight Method with Feature Fusion and Multi-Context for Face Detection

Convolutional neural networks (CNN for short) have made great progress in face detection. They mostly take computation intensive networks as the backbone in order to obtain high precision, and they cannot get a good detection speed without the support of high-performance GPUs (Graphics Processing Units). This limits CNN-based face detection algorithms in real applications, especially in some speed dependent ones. To alleviate this problem, we propose a lightweight face detector in this paper, which takes a fast residual network as backbone. Our method can run fast even on cheap and ordinary GPUs. To guarantee its detection precision, multi-scale features and multi-context are fully exploited in efficient ways. Specifically, feature fusion is used to obtain semantic strongly multi-scale features firstly. Then multi-context including both local and global context is added to these multi-scale features without extra computational burden. The local context is added through a depthwise separable convolution based approach, and the global context by a simple global average pooling way. Experimental results show that our method can run at about 110 fps on VGA (Video Graphics Array)-resolution images, while still maintaining competitive precision on WIDER FACE and FDDB (Face Detection Data Set and Benchmark) datasets as compared with its state-of-the-art counterparts.

Semi-Local Structure Patterns for Robust Face Detection

In many image processing and computer vision problems, including face detection, local structure patterns such as local binary patterns (LBP) and modified census transform (MCT) have been adopted in widespread applications due to their robustness against illumination changes. However, being reliant on the local differences between neighboring pixels, they are inevitably sensitive to noise. To overcome the problem of noise-vulnerability of the conventional local structure patterns, we propose semi-local structure patterns (SLSP), a novel feature extraction method based on local region-based differences. The SLSP is robust to illumination variations, distortion, and sparse noise because it encodes the relative sizes of the central region with locally neighboring regions into a binary code. The principle of SLSP leads noise-robust expansions of LBP and MCT feature extraction frameworks. In a statistical analysis, we find that the proposed methods transform a substantial amount of random noise patterns in face images into more meaningful uniform patterns. The empirical results on the ${\rm MIT} + {\rm CMU}$ dataset and FDDB (face detection dataset and benchmark) show that the proposed semi-local patterns applied to LBP and MCT feature extraction frameworks outperform the conventional LBP and MCT features in AdaBoost-based face detectors, with much higher detection rates.

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.