Abstarct: Aliveness Detection of fingerprint is a new topic in the field of security systems. One method for fingerprint vitality detection is power Spectrum which is baxsed on frequency analysis. In this thesis, Power Spectrum is used for comparing the artificial fingerprints of SFinGe with live ones. The results showed that the real and artificial fingerprints vary in terms of the frequency spectrum. We analyzed the relationship between fingerprints and diseases. We analyzed the fingerprint images of two systematic diseases such as diabetes and addiction. First, we have used the power spectrum method for analysis.The results showed that ought to find other ways to extract the features from fingerprint images. Wavelet transform and extracted features from wavelet coefficients act stronger than the Fourier transform in image analysis. The combination of textural features extracted from the wavelet coefficients with the statistical features of wavelet, will make stronger feature vector. In this thesis, two methods baxsed on statistical characteristics of wavelet and texture features of images have been used for analysis of fingerprint images in patients. These results are promising in detecting relationship between fingerprints with diseases.