## 2 Related Work  
Existing liveness detection approaches are not explicitly designed for mobile devices and are generally unsuited for portable devices [7; 8]. Several software-based methods, including Fourier Transform (FT), Local Binary Patterns (LBP), Binarized Statistical Image Features (BSIF), Local Phase Quantization (LPQ), Weber Local Image Descriptor, or Histograms of Invariant Gradients (HIG), have been investigated for PAD [9; 10; 11]. Deep learning approaches have also been applied. [PERSON] et al. learned a suitable CNN architecture that was evaluated by executing linear SVM on the resulting deep representation [12]. [PERSON] et al. used a hybrid approach combining CNNs and LBPs with best reported accuracy of 95.2% using 50,000 samples for training from the LivDet 2009, 2011, and 2013 datasets [13]. In 2013, [PERSON] et al. [14] discussed a method to capture multiple finger photos in the smallest time frame. In addition, they implemented an anti-spoofing method that relied on the user's challenge response to detect spoofs by measuring the light that was reflected from the finger that was exposed to the camera's LED as well as the position, distance, and sharpness characteristics of the finger. A database of video stream data was built and an overall Equal Error Rate (EER) of 3% on the 37 subjects was reported. In 2014, [PERSON] et al. presented the Mobile Biometric Liveness Detection (MoBio LivDet) approach that analyzes local and global features of biometric images to defend against spoofing [8]. Locally Uniform Comparison Image Descriptor (LUCID), Census Transform Histogram (CENTRIST), and Patterns of Oriented Edge Magnitudes (POEM)were used for being computationally inexpensive and suitable for mobile processors as well as robust to photometric transformations and noise. For fingerprint PAD, the researchers found that LUCID and Local Phase Quantization (LPQ) attained 7.17% and 14.22% Half Total Error Rate (HTER), respectively; while CENTRIST achieved a HTER 2.2% and POEM 3.84%. In this paper, experiments were carried out on sensor-based fingerprint images. In 2016, [PERSON] et al. evaluated LBP, DSIFT, and LUCID on a new database of spoofs that they created as an extension of the previously published IIITD Smartphone Finger photo Database built to study matching algorithms [4]. When the complete dataset is considered, the lowest EER reported is 3.7% and achieved using LBP features to train an SVM classifier, followed by DSIFT with EER = 5.37% and LUCID EER = 22.22%. In 2018, [PERSON] et al. discussed an approach based on the convolution of input images at multiple scales through a Frangi filter to obtain Maximum Filter Response (MFR) images that are then processed to extract LBP, HOG, and BSIF features [15]. The performance obtained by SVM trained using these features was Attack Presentation Classification Error Rate (APCER) of 10%, Attack Presentation Classification Error Rate (BPCER) of 1.8% for print picture attacks, 0.0% for display attacks, and 0.66% for replay attacks. In 2019, [PERSON] et al. discussed the finger photo recognition pipeline, including acquisition from a smartphone, liveness detection, quality assessment, segmentation, enhancement, feature extraction, and matching. The authors concluded that the two main reasons for the low performance of matching are due to heterogeneous smartphone acquisition, uncontrolled illumination, and background conditions' diversity [16]. Recently, [PERSON] et al. investigated a methodology that transforms RGB finger photos into various color spaces, which are used to train different CNNs. The approach finds the best deep representation for each color model and combines the corresponding individual outputs. The overall system's performance combining the top three color spaces achieved an EER of 2.12% [17]. Although multiple color spaces were used on the whole image, there were no experimental validations to prove independence among the color spaces.