Facial Parts Detection Research Articles

SSDMNV2: A real time DNN-based face mask detection system using single shot multibox detector and MobileNetV2

Face mask detection had seen significant progress in the domains of Image processing and Computer vision, since the rise of the Covid-19 pandemic. Many face detection models have been created using several algorithms and techniques. The proposed approach in this paper uses deep learning, TensorFlow, Keras, and OpenCV to detect face masks. This model can be used for safety purposes since it is very resource efficient to deploy. The SSDMNV2 approach uses Single Shot Multibox Detector as a face detector and MobilenetV2 architecture as a framework for the classifier, which is very lightweight and can even be used in embedded devices (like NVIDIA Jetson Nano, Raspberry pi) to perform real-time mask detection. The technique deployed in this paper gives us an accuracy score of 0.9264 and an F1 score of 0.93. The dataset provided in this paper, was collected from various sources, can be used by other researchers for further advanced models such as those of face recognition, facial landmarks, and facial part detection process.

LSTM Based Lip Reading Approach for Devanagiri Script

Speech Communication in a noisy environment is a difficult and challenging task. Many professionals work in noisy environments like aviation, constructions, or manufacturing, and find it difficult to communicate orally. Such noisy environments need an automated lip-reading system that could be helpful in communicating some instructions and commands. This paper proposes a novel lip-reading solution, which extracts the geometrical shape of lip movement from the video and predicts the words/sentences spoken. An Indian specific language data set is developed which consists of lip movement information captured from 50 persons. This includes students in the age group of 18 to 20 years and faculty in the age group of 25 to 40 years . All have spoken a paragraph of 58 words within 10 sentences in Hindi (Devanagari, spoken in India) language which was recorded under various conditions. The implementation consists of facial parts detection, along with Long short term memory’s. The proposed solution is able to predict the words spoken with 77% and 35% accuracy for data set of 3 and 10 words respectively. The sentences are predicted with 20% accuracy, which is encouraging.

Extraction of human face features from color images

Face detection has been widely studied by researchers. However, detection and extraction of human face features is very important as it plays a vital role in variety of applications involving automated face processing. This article focuses on extraction of face parts such as eyes, nose, lips, musta che, and beard on Indian people, for which we have prepared our own face dataset containing variety in faces, from both urban and rural areas. This study focuses on how a detected face part becomes useful in detecting other face parts. We implement our approaches of detecting face parts and evaluate them on our dataset. We exploit YCbCr color model, Viola Jones technique, landmark detection, and level set evolution technique in our approaches of face part detection and extraction. We found that our approaches are effective on extracting face boundary, eyes, nose, and lips and provide comparable results.

Energy Efficient Face Recognition in Mobile-Fog Environment

Proliferation in technological advancements has leveraged the evolution of cell-phones as powerful smart-phones with high computing competence. Integration of sensors and high definition camera has empowered the smart-phones as a tool to solve real world problems. Multiple applications of face recognition have been developed to address the security challenges using the computation power of handheld (mobile) devices. However, one important limitations of mobile devices is energy efficiency. This work has proposed an energy efficient fog-based face recognition using mobile platform. The architecture in proposed system initially performs the facial part detection on mobile device and uploads the selected part by discarding the rest. This reduces the network traffic and attempts to reduce energy consumption. Preliminary results have indicated a saving of approximately 93% of energy in mobile devices.

Masked Face Detection using the Viola Jones Algorithm: A Progressive Approach for less Time Consumption

<p>The use of CCTV surveillance is today’s need in<br />public and private sector for ensuring security against terrorism<br />and robbery. Regular expressions are used to signify enormous<br />sets of motion attributes captured in video. The video vigilance<br />is popular system without using human interference to capture<br />important scenes. The motive of the work is to introduce automatic<br />revelation of masked objects in real time with a surveillance<br />camera. The main aim is to detect masked person automatically<br />in less time period. In this paper,the researcher proposes a system<br />that consists methods which uses four variant steps that are the<br />steps of calculating distance range of person from the camera,<br />eye or vision line detection and face part detection such as<br />mouth detection and face detection. Performance of proposed<br />algorithm is carried out on various real time inputs. Experimental<br />evaluation shows that proposed algorithm exceeds better in terms<br />of time consumption. This unique approach for the problem<br />has created a method transparent and easier in complexity so<br />that the real time implementation can be made beneficial and<br />workable. Analysis of the algorithms fulfillment on the test video<br />track gives appropriate judgments for additional improvements<br />in the masked face detection performance. Finally, based on the<br />research, the axioms were useful for the work which can be<br />usually accessible from available algorithms.</p>

A scheme of features fusion for facial expression analysis: A facial action recognition

The study of emotions is very tough job which sounds in the form to recognize the facial expressions of human face. This problem is quite challenging because of various face architecture of the facial parts of human. In fact it is variant with locality. It becomes hot topic of research for hearing impaired people. Facial expression is considered as a part of sign language recognition (SLR).The pre-processing involves face detection using the algorithm proposed by Viola and Jones using Haar-cascades for facial part detection. In this work, we purpose a fusion of texture local binary pattern, landmark based active shape model (ASM) of geometric features. The features are trained and tested by multi-class support vector machine (SVM).

Detection of Face Direction by Using Inter-Frame Difference

Applying image processing techniques to education, the face of the learner is photographed, and expression and movement are detected from video, and the system which estimates degree of concentration of the learner is developed. For one learner, the measuring system is designed in terms of estimating a degree of concentration from direction of line of learner’s sight and condition of the eye. In case of multiple learners, it must need to measure each concentration level of all learners in the classroom. But it is inefficient because one camera per each learner is required. In this paper, position in the face region is estimated from video which photographs the learner in the class by the difference between frames within the motion direction. And the system which detects the face direction by the face part detection by template matching is proposed. From the result of the difference between frames in the first image of the video, frontal face detection by Viola-Jones method is performed. Also the direction of the motion which arose in the face region is estimated with the migration length and the face region is tracked. Then the face parts are detected to tracking. Finally, the direction of the face is estimated from the result of face tracking and face parts detection.

Face and Eye Recognition on Gray Image using DWT with RBFSVM Method

Facial part detection or extraction shows the most important role in face and eye recognition. In this article proposed a new algorithm for Face and Eye Recognition (FER) using radial basis function support vector machine (RBFSVM) classifier. The discrete wavelet transform (DWT) is used for feature extraction and selection. For the experimental results, used JAFFE and ORL database. In this algorithm, extract face component like left eye, right eye, mouth and nose. In the preprocessing stage, apply median filtering for removing noise from an image. This stage improves the feature extraction process. Finding an image from the image components is a typical task in pattern recognition. The detection rate has reached up to100% for eye recognition and for face recognition is 90-96%. The proposed system estimates the value of precision and recall. This algorithm is compared with SVM and our proposed proved better than previous algorithms.

English

Image mining is defined as the discovery of image patterns in a given collection of images. It is an effort that fundamentally draws upon knowledge in computer vision, image processing, data mining, machine learning, database, and artificial intelligence. Facial recognition helps to analyze and compare the patterns from the facial images. Facial feature extraction is an automatic recognition of human faces by detecting its features i.e. eyes, eyebrows and lips. In this research work, features are extracted from the human facial images by using the existing Face Part Detection (FPD) algorithm and the newly proposed Gray Level Co-occurrence Matrix (GLCM) algorithm. FPD uses bounding box method and GLCM uses affine moment invariants method. Performance factors applied here are feature extraction accuracy and execution time. The implementation of this work is performed in MATLAB 7.0. Based on the experimental results, it is observed that the proposed GLCM algorithm extracted the features more accurately with minimum execution time than FPD algorithm.

Face Detection and Pose Estimation Based on Evaluating Facial Feature Selection

The detection of faces is one of the most requesting fields of research in image processing and Visual estimation of head pose is desirable for computer vision applications such as face recognition, human computer interaction, and affective computing. In this paper, we propose completed method for face pose estimation, face and face parts detection, feature extraction, tracking. This paper proposes using an improved AdaBoost algorithm, which is much better than normal AdaBoost. We use the de-facto Viola-Jones method for face and face part detection. From the robustness property of Haar-like feature, we first construct the strong classifier more effective to detect rotated face, and then we propose a novel method that can reduce the training time. We adopt affine motion model estimation as a tracking method. The combination enables efficient detection around the search area limited by tracking. Experimental results demonstrated its effectiveness and robustness against different types of detection and pose estimation in the input face images, including faces that appear in a wide range of image positions and scales, and also complex backgrounds, occlusions, illumination variations and multi-pose head images.

Non-intrusive liveness detection by face images

A technique evaluating liveness in face image sequences is presented. To ensure the actual presence of a live face in contrast to a photograph (playback attack), is a significant problem in face authentication to the extent that anti-spoofing measures are highly desirable. The purpose of the proposed system is to assist in a biometric authentication framework, by adding liveness awareness in a non-intrusive manner. Analyzing the trajectories of certain parts of a live face reveals valuable information to discriminate it against a spoofed one. The proposed system uses a lightweight novel optical flow, which is especially applicable in face motion estimation based on the structure tensor and inputs of a few frames. For reliable face part detection, the system utilizes a model-based local Gabor decomposition and SVM experts, where selected points from a retinotopic grid are used to form regional face models. Also the estimated optical flow is exploited to detect a face part. The whole procedure, starting with three images as input and finishing in a liveness score, is executed in near real-time without special purpose hardware. Experimental results on the proposed system are presented on both a public database and spoofing attack simulations.

1A2-L10 視覚観測困難な自己の顔の身体像におけるパーツ検出モデルの提案(認知ロボティクス)

1A2-L10 視覚観測困難な自己の顔の身体像におけるパーツ検出モデルの提案(認知ロボティクス)

四方向面特徴と弛緩整合法を用いた顔向きによらない顔部品検出

四方向面特徴と弛緩整合法を用いた顔向きによらない顔部品検出

セキュリティを目的とした画像技術 動領域内の肌色推定による顔領域および顔部品抽出

We propose a method to detect face regions by using the standard skin color which is taken from an image in a color histogram. Extraction of the face regions is based on an area in motion and the deviation from the standard skin color obtained by discriminant analysis. The standard skin color is updated from information obtained from the detected face region. To judge a face, we use templates composed of directional features and color features to detect eyes and mouth. In the face detection experiment, our algorithm could detect a face region by using motion of a subject's head. In addition, the updated standard skin color was correct. In the facial parts detection experiment, by updating the templates, our method worked better than the method that used only directional features. Consequently, our algorithm can extract the facial features even when a person tilts his or her head. With a SGI/O 2 workstation, it is possible to achieve a speed of approximately 0.25 sec/frame.