Gender classification using custom convolutional neural networks architecture

Gender classification demonstrates high accuracy in many previous works. However, it does not generalize very well in unconstrained settings and environments. Furthermore, many proposed Convolutional Neural Network (CNN) based solutions vary significantly in their characteristics and architectures, which calls for optimal CNN architecture for this specific task. In this work, a hand-crafted, custom CNN architecture is proposed to distinguish between male and female facial images. This custom CNN requires smaller input image resolutions and significantly fewer trainable parameters than some popular state-of-the-arts such as GoogleNet and AlexNet. It also employs batch normalization layers which results in better computation efficiency. Based on experiments using publicly available datasets such as LFW, CelebA and IMDB-WIKI datasets, the proposed custom CNN delivered the fastest inference time in all tests, where it needs only 0.92ms to classify 1200 images on GPU, 1.79ms on CPU, and 2.51ms on VPU. The custom CNN also delivers performance on-par with state-of-the-arts and even surpassed these methods in CelebA gender classification where it delivered the best result at 96% accuracy. Moreover, in a more challenging cross-dataset inference, custom CNN trained using CelebA dataset gives the best gender classification accuracy for tests on IMDB and WIKI datasets at 97% and 96% accuracy respectively

Model Fast Tansfer Learning pada Jaringan Syaraf Tiruan Konvolusional untuk Klasifikasi Gender Berdasarkan Citra Wajah

The face is a challenging object to be recognized and analyzed automatically by a computer in many interesting applications such as facial gender classification. The large visual variations of faces, such as occlusions, pose changes, and extreme lightings, impose great challenge for these tasks in real world applications. This paper explained the fast transfer learning representations through use of convolutional neural network (CNN) model for gender classification from face image. Transfer learning aims to provide a framework to utilize previously-acquired knowledge to solve new but similar problems much more quickly and effectively. The experimental results showed that the transfer learning method have faster and higher accuracy than CNN network without transfer learning

Pengenalan Ekspresi Wajah dengan Metode Viola Jones dan Convolutional Neural Network

Currently, the use of artificial intelligence is growing rapidly, including being used to recognize human facial expressions. Human facial expressions have a complex recognition rate. In this study, deep learning will be applied to find out how much accuracy the recognition of facial expressions. The method used in this study is a combination of Viola Jones and Convolutional Neural Network. Viola Jones is used at the segmentation stage and Convolutional Neural Network to classify data. The facial expression dataset that was analyzed consisted of happiness, anger, disgust, sadness, fear, surprise and normal totaling 2205 data. Tests conducted using a conffusion matrix with an accuracy rate of 96.14%. The results of this test indicate that the proposed method has good accuracy for recognizing facial expressions.Saat ini penggunaan kecerdasan buatan berkembang dengan pesat, diantaranya dimanfaatkan untuk mengenali ekspresi wajah manusia. Ekspresi wajah manusia memiliki tingkat pengenalan yang kompleks. Pada penelitian ini akan diterapkan deep learning untuk mengetahui seberapa besar tingkat akurasi dalam pengenalan ekspresi wajah. Metode yang digunakan dalam penelitian ini yaitu gabungan Viola Jones dan Convolutional Neural Network. Viola Jones digunakan pada tahap segmentasi dan Convolutional Neural Network untuk mengklasifikasi data. Dataset ekspresi wajah yang dianalisis terdiri dari bahagia, merah, muak, sedih, takut, terkejut dan normal sejumlah 2205 data. Pengujian yang dilakukan menggunakan confussion matrix dengan tingkat akurasi sebesar 96,14%. Dari hasil pengujian ini menunjukan bahwa metode yang diusulkan memiliki akurasi yang baik untuk mengenali ekspresi wajah