# Face Spoofing Detection with Stacking Ensembles in Work Time Registration System
## 2 Review of Existing Solutions
[26] proposed a two-stream convolutional neural network (TSCNN) that works in two complementary spaces: RGB and multi-scale retinex (MSR) space to take advantage of both texture details in RGB space and illumination invariance of MSR. [PERSON] and [PERSON] [27] developed an approach to convert the image to the L*a*b* color space, which is then passed through HOG (Histogram of Oriented Gradients) for face detection. The detected face is then advanced through the VGG7 CNN architecture for the extraction of features and then classified using a fully connected layer for spoof detection. [PERSON] et al. [28], instead of completely extracting handcrafted texture features or relying only on deep neural networks, addressed the problem by fusing both wide and deep features in a unified neural architecture. The main idea was to take advantage of the strengths of both methods to derive a well-generalized solution for the problem. [PERSON] [9] performed face detection using MTCNN (Multi-Task Cascaded Convolutional Neural Network) and used the Support Vector Classifier (SVC) to classify real and fake faces. [PERSON] et al. [29] presented an Online Attendance System Using Face Recognition to record the daily attendance of students in online classes. [PERSON] et al. [30] proposed a model that consists of two streams. One stream converts the input RGB images into grayscale ones and conducts multi-scale color inversion to obtain the MSCI images, which are then put into the MobileNet to extract face reflection features. The other stream directly feeds RGB images into the MobileNet to extract face color features. Finally, the features extracted separately from the two branches are fused and then used for face spoofing detection. [PERSON] et al. [31] presented face spoofing detection based on the extraction of Heterogeneous Auto-Similarities of Characteristics (HASC) descriptor from the HSV and YCbCr color spaces. The HASC descriptor encodes dependencies between low-level dense features of the color face image using covariance and information-theoretic measures to explore how the features are interrelated in real and fake faces. [PERSON] et al. [32] proposed Dual Probabilistic Modeling (DPM), with two dedicated modules, DPM-LQ (Label Quality aware learning) and DPM-DQ (Data Quality aware learning). Both modules were designed based on the assumption that data and labels should form coherent probabilistic distributions. The task of DPM-LQ was to produce feature representations without overfitting to the distribution of noisy semantic labels, and the task of DPM-DQ was to eliminate data noise from 'False Reject' and 'False Accept' during inference by correcting the prediction confidence of noisy data based on its quality distribution. [PERSON] et al. [33] since the vision transformer (ViT) has demonstrated significant performance gains in various computer vision tasks by effectively capturing long-range dependencies, recent FAS methods have investigated the potential of leveraging the channel-wise features derived from self-attention (SA) in ViT. While channel-wise features effectively capture discriminative local attention, the complementary information existing between different channels remained undiscovered in prior methods. This paper investigates the unexplored characteristics of complementary channel information within ViT and proposes to incorporate both channel-wise and complementary channel information to learn long-range and discriminative features for FAS. Two modules, Channel Difference Self-Attention (CDSA) and Multi-head Channel Difference Self-Attention (MCDSA), are used to facilitate learning complementary channel characteristics and enhancing both feature discriminability and representational capacity. Using CDSA and MCDSA, the paper presents a custom Channel Difference Transformer (CDformer). [PERSON] et al. [34] provide a comprehensive review of the state-of-the-art works published over the past decade and discuss the temporal evolution of the FAS/PAD field.