## 1 Introduction  
This research promotes the secure adoption of mobile technology in homeland security and across the federal government. Although smartphones have developed into highly portable and accelerated computing devices, there is a growing challenge to secure them properly. Due to lower costs and geographic flexibility, several government services can benefit from using mobile devices. Samsung and German Federal officials have been working on secure storage public credentials, and user-friendly smartphone devices [1]. Samsung has been cooperating with the German Federal Office for Information Security (BSI) to enable secure storage of ID credentials on Galaxy smartphones, which will combine the high-level trust in the physical document with the user-friendliness of smartphones [1]. Furthermore, staff-less stores are predominantly trending where customers are connected via a mobile application for authentication. Recent advances have shown that finger photos acquired using a basic smartphone camera are promising for authenticating individuals. At FMR = 0.1%, finger photo-to-finger photo matching has achieved an accuracy of 99.66% by fusing four fingers and 85.62% by using individual fingers [2]. Very recently, the contactless finger photo to contact-based fingerprint matching has achieved an Equal Error Rate (EER) less than 1% across multiple datasets and under background and illumination variation [3]. Researchers have also established the vulnerability of finger photo-based systems to presentation attacks (PAs) [4], see an example in Figure 1. The PAs considered in this study are compared with printouts and multiple devices such as iPads and laptops. In mobile devices, the sensor is a component of the architecture that lacks a mechanism for preventing spoofing. Thus, the acquisition sensor requires a Presentation Attack Detection (PAD) module, which classifies biometric samples as being live (non-spoof) or fake (spoof). Only a few research efforts have been made to mitigate this issue. There is an urgent need to build robust PAD modules to classify genuine biometric samples from presentation attacks. The proposed research seeks to boost the performance of finger photo PAD using deep fusion strategies by investigating whether the robustness of PADs to different spoofing methods can be enhanced by fusing various color spaces (e.g., HSV, YCbCr). From the image classification literature, it is known that certain classes might be better represented in specific color spaces [5]. Most of the PAs are generated using different hardware inclined toward different color spaces. Color monitors mostly use RGB color space, CMY models for color printout, and HSV, which are the closest to how humans interpret colors, and YCbCr is paired with popular video and digital photography. A color space is a specific organization of colors to produce digital color representation [5]. RGB is ideal for image color generation (as in image capture by a color camera), but its use is limited to the color description. Thus, researchers are also investigating transformations of original RGB images into different color spaces, and their impact on accuracy [5]. In RGB, a spoof can only be modeled in terms of percentages of the three primaries composing its color [6]. Differences between live human fingers and display attacks can be better detected using good color descriptors. Choosing an appropriate color space can provide a more robust analysis. Images of the color models used in the proposed study are shown in Figure 2.  
Figure 1: An example of a finger photo display attack. A finger photo of the genuine identity is displayed by using a smartphone positioned in front of the camera integrated in the mobile to unlock. Figure 2: Examples of finger photo center core patches converted into different color spaces. Finger photos are featured by qualities such as the color of the skin and ridges of fingerprints; thus, working with various color spaces will help to focus on characteristics that cannot be extracted from RGB images.