A gender recognition system from facial images using SURF based BoW method

2017 ◽  
Author(s):  
Bahar Hatipoglu ◽  
Cemal Kose
Keyword(s):  
Recognition System ◽  
Gender Recognition ◽  
Facial Images

scholarly journals A Gender Recognition System Using Facial Images with High Dimensional Data

2021 ◽  
Vol 6 (1) ◽  
pp. 27-45
Author(s):  
Martins E. Irhebhude ◽  
Adeola O. Kolawole ◽  
Hauwa K. Goma
Keyword(s):  
Female Gender ◽  
Recognition System ◽  
Research Area ◽  
High Dimensional ◽  
Svm Classifier ◽  
Gender Recognition ◽  
Success Rates ◽  
Histogram Of Oriented Gradient ◽  
The Public ◽  
Facial Images

Gender recognition has been seen as an interesting research area that plays important roles in many fields of study. Studies from MIT and Microsoft clearly showed that the female gender was poorly recognized especially among dark-skinned nationals. The focus of this paper is to present a technique that categorise gender among dark-skinned people. The classification was done using SVM on sets of images gathered locally and publicly. Analysis includes; face detection using Viola-Jones algorithm, extraction of Histogram of Oriented Gradient and Rotation Invariant LBP (RILBP) features and trained with SVM classifier. PCA was performed on both the HOG and RILBP descriptors to extract high dimensional features. Various success rates were recorded, however, PCA on RILBP performed best with an accuracy of 99.6% and 99.8% respectively on the public and local datasets. This system will be of immense benefit in application areas like social interaction and targeted advertisement.

scholarly journals A rough set theory and deep learning based predictive system for gender recognition using audio speech

2021 ◽  
Author(s):  
Ghazaala Yasmin ◽  
ASIT KUMAR DAS ◽  
Janmenjoy Nayak ◽  
S Vimal ◽  
Soumi Dutta
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Set Theory ◽  
Rough Set ◽  
Gender Discrimination ◽  
Rough Set Theory ◽  
Recognition System ◽  
Gender Recognition ◽  
Learning Techniques

Abstract Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

scholarly journals Improving the Neural Network Algorithm for Assessing the Quality of Facial Images

2020 ◽  
pp. paper28-1-paper28-12
Author(s):  
Nikita Lisin ◽  
Alexander Gromov ◽  
Vadim Konushin ◽  
Anton Konushin
Keyword(s):  
Neural Network ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Experimental Studies ◽  
Recognition System ◽  
Surveillance Systems ◽  
Neural Network Algorithm ◽  
Baseline Algorithm ◽  
Facial Images

The paper considers the task of obtaining a quality assessment of facial images for usage in various video surveillance systems, video analytics, and biometric identification. The accuracy of person recognition and classification depends on the quality of the input images. We consider an approach to obtaining single face image quality assessment using a neural network model, which is trained on pairs of images that are split into two possible classes: the quality of the first image is better or worse than the quality of the second one. Two modifications of the selected baseline algorithm are proposed. A face recognition system is applied to change the loss function and image and face quality attributes are used when training the model. Experimental studies of the proposed modifications show their effectiveness. The accuracy of selecting the best and worst frame is increased by 1.3% and 1.9%, respectively.

scholarly journals Real-Time Attendance and Feedback System

2021 ◽  
pp. 1135-1139
Author(s):  
Piyush Manish Sonar ◽  
Aniket Nitin Chaudhari ◽  
Mehul Deepak Sethi ◽  
Tejaswini Sanjay Gadakh
Keyword(s):  
Face Recognition ◽  
Principal Component ◽  
Feedback System ◽  
Recognition System ◽  
Fingerprint Recognition ◽  
Student Attendance ◽  
Feedback Form ◽  
Face Recognition System ◽  
User Friendly ◽  
Facial Images

Face is the representation of one’s identity. Hence, we have proposed an automated student attendance system based on face recognition. Face recognition system is very useful in life applications especially for attendance system. In our proposed approach, firstly, video framing is performed by activating the camera through a user-friendly interface. In the pre-processing stage, scaling of the size of images is performed, if necessary, in order to prevent loss of information. In face recognition stage, enhanced local binary pattern (LBP) and principal component analysis (PCA) is applied correspondingly in order to extract the features from facial images. Another way of marking the attendance is fingerprint recognition. To mark the attendance students simply have to give the fingerprint impression in fingerprint scanner module. Finally, the attendance of the recognized student will be marked and saved in the excel file. The student who is not registered will also be able to register on the spot and notification will be given if students sign in more than once. Whenever seminar is completed then a link is sent on email. It includes the information in terms of feedback. When student fills the feedback form then analysis of overall session is done.

An effective real time gender recognition system for smart cameras

2019 ◽  
Vol 11 (6) ◽  
pp. 2407-2419 ◽  
Author(s):  
Vincenzo Carletti ◽  
Antonio Greco ◽  
Alessia Saggese ◽  
Mario Vento
Keyword(s):  
Real Time ◽  
Recognition System ◽  
Gender Recognition ◽  
Smart Cameras

A Novel Hybrid Metaheuristic Approach to Perceive Gender Based Identification System

2020 ◽  
Vol 13 ◽  
Author(s):  
Aparna Shukla ◽  
Suvendu Kanungo
Keyword(s):  
Search Space ◽  
Recognition System ◽  
High Dimensional ◽  
Identification System ◽  
Hybridization Technique ◽  
Optimization Approach ◽  
Gender Recognition ◽  
Accuracy Rate ◽  
Tree Classifier ◽  
Gender Based

Background: Gender recognition is one of the most challenging perceptible tasks that receiving attention in the increasing digital data era as the requirement of personalized, reliable and ethical system inevitable. A problem that we address in this paper, greatly deals with the gender based identification system. We are motivated by this problem as many recent social interactions and existing services rely on the gender of an individual, and also in forensic identification, the gender information provides the feasibility for easy and quick investigation. Objective: The paper primarily focused on the gender based identification problem and culminate a robust gender based recognition system with the higher accuracy rate. We attempted to perceive the gender of an individual through the multimodal biometric system by integrating the three prominent biometric traits namely: fingerprint, palm-print and hand in a specific manner. The proposed multimodal biometric for gender recognition system provides a better accuracy rate improvement with the optimal feature set which are generated from available high dimensional features set. Method: Aiming for the objective to reduce the search space, a hybrid meta-heuristic approach GSA-Firefly (GFF) is introduced in this paper. The optimization approach GFF is proposed to retrieve the optimal number of features from the high dimensional features generated by fusing the texture features of all the three considered biometric traits along with the fingerprint minutiae features. Further, the decision tree classifier is used to classify the gender of an individual. Results: The feasibility of the proposed approach is measured with different qualitative performance parameters. In light of achieving the accuracy rate of 99.2%, it shows that its performance comparatively better against other techniques reported in the literature with the different sets of classier. Conclusion: The hybridization technique that effectively integrate meta-heuristic approaches GSA and firefly outperforms other similar approaches with respect to obtaining the optimal features of multimodal biometric for gender based identification system. Further, the novel technique enhance the overall performance of the system by reducing the search space over time and space.

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