Average Neural Face Embeddings for Gender Recognition

Makinist, Semiha; Ay, Betül; Aydın, Galip

doi:10.31590/ejosat.araconf67

Cited by 6 publications

(2 citation statements)

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“…FaceNet uses face images as input and produces a vector of 128 numbers that represent the most salient facial features. These vectors are known as "embeddings" in machine learning [16]. The embedding process involves transferring all journal.ump.edu.my/ijsecs ◄ important data from an image to a vector.…”

Section: Facenet Facial Feature Extractionmentioning

confidence: 99%

Twofold Face Detection Approach in Gender Classification Using Deep Learning

Mustapha¹,

Mohamad²,

Hamid³

2023

IJSECS

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Face classification is a challenging task that is crucial to numerous applications. There are many algorithms for classifying gender, but their ability to evaluate their effectiveness regarding scientific data is constrained. Deep learning is popular among researchers in face classification problems. The detection of many faces is complicated and becomes a necessity in real problems. The proposed research aims to examine the effect of twofold face detection approach on the accuracy of gender classification, as well as the effect of using small datasets on accuracy. In this study, we use a small dataset to classify facial images based on their gender. The following phases involve deep learning methods along with the OpenCV library version 3.4.2 which is recommended to serve as a twofold face detection approach. In the experiments conducted, Phase 1 is the designated training phase, and Phase 2 serves as a testing phase. Two different algorithms are used in the testing phase to detect one face in the image (Experiment 1), while the remaining algorithm detects multiple faces in the image (Experiment 2). The FEI dataset is used to evaluate the accuracy of the proposed research, which results in 84% accuracy for Experiment 2 and 74% for Experiment 1, respectively.

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Section: Facenet Facial Feature Extractionmentioning

confidence: 99%

Twofold Face Detection Approach in Gender Classification Using Deep Learning

Mustapha¹,

Mohamad²,

Hamid³

2023

IJSECS

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“…In [24] the authors use a multi-task framework for facial attributes classification through end-to-end face parsing and deep CNN, they address three challenging problems of race, age, and gender recognition. In [25] authors use an Average Neural Face Embeddings (ANFE) method that uses facial vectors of people for gender recognition. In [2] we present a model which was pre-trained on face sketch gender classification and recognition task.…”

Section: Literature Reviewmentioning

confidence: 99%

Golden Ratio and Its Application to Bayes Classifier Based Face Sketch Gender Classification and Recognition

Ounachad¹

2020

IJETER

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Machine learning is a subarea of artificial intelligence based on the idea that systems can learn from data and make decisions automatically. Bayes Theorem is widely used in machine learning. The main objective of this paper is to classify the gender of the human being based on their face sketch images by using a golden ratio features and Bayes Classifier. This paper presents a method for human face sketch gender classification and recognition. It is inspired in our other model which was pre-trained on the same task, but with sixteen features and fuzzy approach. Toward this end, just two features will be extract from the input face sketch image based on two face golden ratios. The detection stage passes by Viola and Jones algorithm. The classification task is evaluated through Bayes classifier. An experimental evaluation demonstrates the satisfactory performance of our approach on CUFS database with 80% for training, 20% for testing. The proposed machine learning algorithm will be a competitor of the proposed relative the stat of the art approaches.

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