Gender Recognition is an attractive research area in recent years. To make a user-friendly application for Gender Recognition, having an accurate, fast, and lightweight model applicable in a mobile device is necessary. Although successful results have been obtained using the Convolutional Neural Network (CNN), this model needs high computational resources that are not appropriate for mobile and embedded applications. To overcome this challenge and considering the recent advances in Deep Learning, in this paper, we propose a deep learning-based model for Gender Recognition in mobile devices using the lightweight CNN models. In this way, a pretrained CNN model, entitled Multi-Task Convolutional Neural Network (MTCNN), is used for face detection. Furthermore, the MobileFaceNet model is modified and trained using the Margin Distillation cost function. To boost the model performance, the Dense Block and Depthwise separable convolutions are used in the model. Results on six datasets confirm that the proposed model outperforms the MobileFaceNet model on six datasets with the relative accuracy improvements of 0.02%, 1.39%, 2.18%, 1.34%, 7.51%, 7.93% on the LFW, CPLFW, CFP-FP, VGG2-FP, UTKFace, and own data, respectively. In addition, we collected a dataset, including a total of 100’000 face images from both male and female in different age categories. Images of the women are with and without headgear.

Emotions are human mental states at a particular instance in time concerning one’s circumstances, mood, and relationships with others. Identifying emotions from the whispered speech is complicated as the conversation might be confidential. The representation of the speech relies on the magnitude of its information. Whispered speech is intelligible, a low-intensity signal, and varies from normal speech. Emotion identification is quite tricky from whispered speech. Both prosodic and spectral speech features help to identify emotions. The emotion identification in a whispered speech happens using prosodic speech features such as zero-crossing rate (ZCR), pitch, and spectral features that include spectral centroid, chroma STFT, Mel scale spectrogram, Mel-frequency cepstral coefficient (MFCC), Shifted Delta Cepstrum (SDC), and Spectral Flux. There are two parts to the proposed implementation. Bidirectional Long Short-Term Memory (BiLSTM) helps to identify the Gender from the speech sample in the first step with SDC and pitch. The Deep Convolutional Neural Network (DCNN) model helps to identify the emotions in the second step. This implementation is evaluated with the help of wTIMIT data corpus and gives 98. 54% accuracy. Emotions have a dynamic effect on Genders, so this implementation performs better than traditional approaches. This approach helps to design online learning management systems, different applications for mobile devices, checking cyber-criminal activities, emotion detection for older people, automatic speaker identification and authentication, forensics, and surveillance

Voiced-based age detection and Gender Recognition are important problems in the telephone speech processing to investigate the identity of an individual. In this paper, a new Gender and age Recognition system is introduced based on the generative incoherent models learned using sparse non-negative matrix factorization and the atom correction step as a post-processing method. The proposed classification algorithm includes training step to provide the appropriate trained atoms for each data class and also the test phase to assess the classification performance. Since the classification accuracy depends highly on the selected features, the Mel-frequency cepstral coefficients are employed to train basis for the better representation of the voice structure. These bases are learned over the data of male and female speakers using non-negative matrix factorization with the sparsity constraint. Then, atom correction is carried out using an energy-based algorithm to decrease the coherence between different categories of the trained dictionaries. In the sparse representation of each data class, the atoms related to other sets with the highest energy are replaced with the lowest energy bases if the reconstruction error does not exceed from a specified limit. The experimental results showed that the proposed algorithm performs better than the earlier methods in this context especially in the presence of background noise.

Background: Determining the genetic Gender has been a human's inaccessible will during the history. Today science resolves the problem; pre implantation diagnosis (PGD) is used as a method of genetic disease diagnosis and Gender determination. The holy Quran in many verses ascribes genetic Gender determination and Recognition to Allah, the Almighty. Hence, according to some, genetic Gender determination is a divine action and human interfering with this process is in direct contradiction to the presumption of divine creativeness and is interference with divine wisdom. In other hand, some believe that though it originally is due to Allah, its substances and Materials causes have been bestowed to people by Allah. The present survey studies the theoretical problems of this modern technique and its contact with divine knowledge and creativeness. We aim to study and criticize the proposed contradictions between this technology and divine presumptions and affairs.Materials and Methods: Basic, Desk, Analytical.Results: 1. Allah's will and decree is prior to all affairs and so all natural and Materials causes solely by the will of Allah could be effective.2.Knowing divine presumptions of the universe order, the modern technology of genetic Gender determination and Recognition not only is not interfering with Allah's actions and contrary with divine presumptions but also is a sign of being human the successor of Allah and a sign of being heavens and the earth at his control and disposal, so long as there is neither interfering with divine affairs nor change of divine legal rules. 3. The holy Quranic verses (related to divine creativeness) aim to show the power of Allah the Almighty, not to negate the effective causes of determining and recognizing the genetic Gender.4. Whatever found in traditions encouraging couples to eat some special foods, because of the Gender and other characters of the embryo, could connote the human's power in such cases.Conclusion: 1. Allah's will and decree is prior to all affairs and so all natural and Materials causes solely by the will of Allah could be effective.2. Knowing divine presumptions of the universe order, the modern technology of genetic Gender determination and Recognition not only is not interfering with Allah's actions and contrary with divine presumptions but also is a sign of being human the successor of Allah and a sign of being heavens and the earth at his control and disposal, so long as there is neither interfering with divine affairs nor change of divine legal rules.3. The holy Quranic verses (related to divine creativeness) aim to show the power of Allah the Almighty, not to negate the effective causes of determining and recognizing the genetic Gender.4.Whatever found in traditions encouraging couples to eat some special foods, because of the Gender and other characters of the embryo, could connote the human's power in such cases.