# Multi-Modal Long-Term Person Re-Identification Using Physical Soft Bio-Metrics and Body Figure
## 2 Related Work
### CNN-Based Multi-Modal Deep Learning for Person Re-Identification  
[PERSON] et al. [8] propose a multi-modal human recognition method that uses both face and body to recognize a person. The proposed model is a fusion of two networks VGG Face-16 and ResNet-50. VGG Face-16 is responsible for processing the facial regions in the image. ResNet-50 is used for processing the body regions in the image. VGG Face-16 is a suitable fit for the task since it is a deep CNN model that was trained on a database for face recognition task [9]. They used ResNet-50 since the body region has more texture, color, and shape. The final decision of the model is formed using score-level fusion and the weighted sum rule is applied to improve the recognition performance.  
The harmonious attention CNN (HACNN) was proposed [10] in 2018. In their work, they present a novel idea of learning attention selection features jointly with feature representation. In an attempt to optimize the person re-identification task using a lightweight architecture with fewer parameters.  
Multi-level factorisation Net (MLFN) presented by [11] in 2018. The novel learning process presented in their work depends on learning identity-discriminative and view-invariant visual factors at multiple semantic levels.  
Spatial and channel partition representation network (SCR) proposed by [12] in 2020. Their proposed framework exploits both spatial and channel information. Their pipeline starts with a strong backbone network ResNet-50. They applied some modifications to ResNet-50 to extract more that e high-level features can be kept in the feature map. The modifications are as follows: (a) replacing the down-sampling done using stride-2 convolution by a stride-1 convolution in the conv5 1 layer, (b) all the layers after conv4, 1 layer are duplicated to form 3 independent branches. The backbone feature extractor network is followed by Multiple Spatial-channel Partitions in a Pyramidal structure which is applied to utilize all the information in the feature map. A pyramid structure is used to train global, spatial and channel partitioned features separately. Experiments to evaluate SCRNet were conducted on three state-of-the-art datasets Market-1501, DukeMTMC-Reid, and CUHK03, and one video-based dataset. SCRNet outperforms the previous state-of-the-art in both single and cross-domain re-identification tasks.  
ResNet50 is an advancement of the original ResNet model (short for Residual Networks) has 48 convolutional layers along with 1 MaxPool and 1 Average Pool layer. ResNets are used as a backbone for several computer vision tasks and are known to achieve remarkable results [13]. Deep networks are hard to train because of the notorious vanishing gradient problem, as the gradient is back-propagated to earlier layers, repeated multiplication may make the gradient extremely small. As a result, as the network goes deeper, its performance gets saturated or starts degrading rapidly. ResNets provide a framework that made it possible to train extremely deep neural networks (150+ layers) that can contain hundreds or thousands of layers and still achieve exceptional performance. The contribution ResNets made to address the vanishing/exploding gradient problem by introducing skip connections allowing the gradient to flow through an alternate shortcut path. Skip connections allow the model to learn an identity function which ensures that the higher layer will perform at least as good as the lower layer, and not worse [14].