# Emotional Health Detection in HAR: New Approach Using Ensemble SNN
## 2 Related Works
In particular, they designed a neural network model called \"D+GAN\" (Depth plus Generative Adversarial network), with which they performed multi-conditional translation from image to image with facial attributes. Compared to the normal two-component GANs generator and discriminator, this network had the advantage of generating high-quality face depth maps by making greater use of facial attribute information and determining sex, age, and race categories. The facial recognition process was divided into the following phases:  
* Capturing RGB face images. * Image preprocessing to remove the image background from the face evenly. * Generating face depth maps
* Merging images using the unsampled Shearlet transform (NSST). * Using different tools for face recognition. The Bosphorus 3D Face Database and the CASUA 3D Face Database were used to train the model, and the BU-3 DFe Database was used for testing. To evaluate the quality of the depth maps obtained, face depth maps were created for each of the previous datasets using different techniques:  
* Monodepth2
* DenseDepth Method (KTTI)
* DenseDepth (NYUDepth)
* DenseDepth (NYUDepthV2)
* 3 DMorphable Model (3 DMMI)
* Pix2 Pix
* CycleGAN  
Numerous experiments were carried out to validate the model. The authors compared the eight depth maps obtained with the techniques cited for each dataset used. The results showed that the outputs generated by D+GAN show more detailed depth information for all three datasets. Even in the correlation graphs of the various output images, there was a higher quality for the depth maps generated by 3 DMM, Pix2 Pix, Cycle GAN, and D+GAN. For a quantitative analysis of the face depth maps generated by the different models, SSIM, RMSE (root-mean-squared error), and PSNR (peak-signal-to-noise ratio) were used. SSIM is a parameter used for assessing the structural similarity of images. It is used to evaluate the quality of the processed image compared to the reference one. Additionally, from a quantitative point of view, these three indices for D+GAN depth maps were the best. The four models, PCA, ICA, Facenet, and InsightFace, were used as face recognition methods. ENTL, Yale, UMIST, AR, and ERET were used as datasets. From the experiments, it was found that for each dataset of the five used, the best results were obtained when operating with the pseudo-RGB-D facial recognition modes compared to the RGB mode. The best result was obtained when combining the FaceNet model with the ORL database. [PERSON] et al. [14] developed an innovative model of recognition of human emotions using analyses of both physiological and textual characteristics considering heart rate [15] and blood pressure, or changes in pupil size or even textual analysis. Their study analyzed five emotions: anger, sadness, joy, disgust, and fear. The model was built taking into account two physiological characteristics: facial muscle movements and HRV combined with textual analysis. Heart rate variability (HRV) is the oscillation of heart rate over a series of consecutive heartbeats. This study's novelty lay in the analysis of the combination of these characteristics with the classification of emotions obtained by applying a deep learning model based on RNN (Recurrent Neural Network). The methodological approach used to collect the characteristics corresponding to each of the five classes of emotions mentioned above involved showing 500 subjects belonging to different cultural backgrounds and age groups a series of photograms of various films to arouse emotions, which were the object of the study. The data on HRV were captured using a wearable device containing various biometric sensors such as a heart rate monitor, blood pressure measurement sensor, and body temperature sensor. The parameters measured were heart rate, systolic blood pressure, and diastolic blood pressure concerning the above categories of emotions. Sixty-eight reference points have been identified on facial images to extract features from facial muscle movement.