Two-branch Network with Feature Fusion for Time Since Deposition Estimation of Bloodstains

In bloodstain examination of collaborative medicine and forensics, the analysis and identification of time since deposition (TSD) plays a significant role. Traditional bloodstain analysis methods can only provide a rough estimate for the TSD of traces, and they are time-consuming. To address this issue, we propose a lightweight framework called Fourier Transform Infrared Network (FTIR-Net) that combines wavelet transform with deep learning. To be specific, we parallelly perform wavelet transform on infrared spectra and compute its second derivative to attain the sequential signal and spectral image. Then, the learning component employs two separate branches to extract features from the one-dimensional (1D) spectra signal and two-dimensional (2D) coefficient images provided by continuous wavelet transform (CWT). To effectively aggregate information from the spectral image, we design a Squeeze-and-Excitation Network (SENet) and combine it with 2D convolution. Finally, the extracted features are concatenated and flattened, followed by two fully connected (FC) layers for retention time analysis. Since the standard bloodstain dataset is lacking, we create a dataset that associates bloodstain with the attenuated total reflectance of Fourier transform infrared (ATR-FTIR). To demonstrate the effectiveness of our model in bloodstain analysis and exploit the properties of the proposed dataset, we present comprehensive experiments and ablation studies.