## 1 Introduction  
Fingerprints are widely recognized as one of the most commonly utilized and acceptable features for personal identification [1, 2, 3, 4]. This is explained by two reasons: firstly, fingerprints are one-of-a-kind and permanent in a person's life, making them ideal long-term indicators of human identity [5, 6]; secondly, fingerprints have one of the highest degrees of dependability among all biometric markers [7, 8], and forensic specialists have employed them extensively in criminal investigations [9]. Fingerprint technologies are rapidly being used for user authentication in a variety of civilian and business applications [10]. Most fingerprint matching methods use global or local fingerprint features, and feature extraction is highly affected by the integrity and quality of fingerprint images [11, 12]. Fingerprint features include ridge and valley clarity, as well as minutiae, core, and delta points [13, 14]. Although the classic power spectrum method is frequently used for fingerprint image quality assessment [15], an algorithm that precisely and rapidly assesses the subjective quality of a fingerprint image still is an important and ongoing objective [16]. Fingerprint Image Quality Assessment (FIQA) has attracted efforts from both academic and industrial areas. The existing studies can be classified into three main types of approaches [17, 18, 19], namely, (a) single feature approaches, (b) segmentation approaches, and (c) multi-feature approaches. Single feature approaches are generally carried out in two ways: global methods and local methods. To assess the quality, global methods examine the entire fingerprint image, whereas local methods extract features from each block and assess quality from those features. [PERSON] et al. [20] proposed an approach based on the Fourier spectrum of the fingerprint image as well as reviewing three approaches based on local standard deviation [21], directional contrast of local block [22], and the Gabor feature [23]. Segmentation approaches are based on segmenting the directional block and non-directional block with a threshold [24]. The fingerprint image quality matrices are represented as a ratio of the area of directional blocks to the image. However, the threshold of directional blocks may differ for different sensors. [PERSON] et al. [23] proposed a technique that employs 8-directional Gabor filters to segment the foreground from the image. Multi-feature approaches combine different fingerprint image quality assessment approaches and classify the blocks into variant levels with thresholds. NIST fingerprint image quality (NFIQ) was proposed by [PERSON] et al. [25]. This approach classifies results into five levels via a trained model of a neural network and estimates a matching score utilizing 11 dimensional features including ridge orientation flow, local ridge curvature, and local contrast. Although the abovementioned approaches show a significant FIQA improvement, the complicated redesign has a higher cost and is time-consuming. For instance, learning-based methods heavily rely on data-driven techniques. Namely, they require a massive amount of labeled data and utilize pretrained networks [26]. This is time-consuming and requires additional computer power. Therefore, a modification of the conventional algorithm (i.e., Power Spectrum) would enable an efficient development to improve FIQA. In this study, we developed a method for quantitatively and objectively assessing image quality derived from the classic Power Spectrum method, which is frequently used for fingerprint image quality [15]. Its main advantage is that it eliminates reliance on specific calibration images, such as Slanted-edge, Siemens star, and sinusoidal pattern (Figure 1). It is unnecessary to capture the image-specific feature targets, such as contour lines and edges as well as corners [15].