## 3 Comprehensive Classification of Existing De-identification Methods
[55] proposed a live face de-identification method that automated video modification at high frame rates. [PERSON] et al. [56] proposed a utility preserving facial image de-identification using appearance subspace decomposition method. They showed that the de-identified faces preserved expressions of the original images while preventing face identity recognition. The system kept the perception, such as pose, expression, lip articulation, illumination, and skin tone identical. The summary of the above discussion is presented in Table 3.  
_Traditional biometrics preserving de-identification_ focuses on de-identifying soft biometrics such as gender, age, race, and ethnicity while preserving as much utility as possible from a traditional biometric trait (such as face, iris, and voice, etc.). [PERSON] and [PERSON] [57] introduced the method where a face image was modified but it remained recognizable, while the gender information was concealed. [PERSON] et al. [58] devised an ad-hoc image filtering-based method for eliminating gender information from the fingerprint images of the subjects, retaining the matching performance as it is. In [59], the authors proposed an automatic hair color de-identification system preserving face biometrics. The system segmented the image hair area and altered basic hair color for natural-looking de-identified images. [PERSON] and [PERSON] [60] proposed a technique that perturbed a face image so that the gender was changed by using a gender classifier while the face recognition capacity was  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  
preserved. They used a warping technique to simultaneously modify a group of pixels by using the Delaunay Triangulation application on facial landmark points. The authors have experimented the system using two gender classifiers namely, IntraFace [61] and Commercial-off-The-Shelf (G-COTS) software. [PERSON] et al. [62] extended this idea by putting forward a convolutional autoencoder, which could modify an input face image to protect the privacy of a subject. They suggested an adversarial training scheme that was expedited by connecting a semi-adversarial module of a supplementary gender classifier and a face matcher to an autoencoder. The authors further tackled the generalizability of the proposed Semi Adversarial Networks (SANs) through arbitrary gender classifiers via the establishment of an ensemble SAN model, which generates a different set of modified outputs for an input face image. Later, in 2020, [PERSON] et al. [18] proposed a GAN-based SAN model, called PrivacyNet, which is further advanced to impart selective soft biometric privacy to several soft biometric attributes such as gender, age, and race. They showed that PrivacyNet provides a condition for users to decide which attributes should be obfuscated and which ones should remain unchanged. [PERSON] et al. [63] proposed an adversarial perturbation-based de-identification algorithm, which anonymized k-facial attributes to remove gender, race, sexual orientation, and age information, preserving the identification ability of the face biometrics. [PERSON] et al. [64] proposed a soft biometrics privacy-preserving system to hide binary, categorical, and continuous attributes from face biometric templates using an incremental variable elimination algorithm. [PERSON] [65] applied face morphing to remove the gender identification attributes from face images. The identification ability of the face images as face biometrics was preserved. An interesting direction of research is focused on tattoo de-identification. A unique tattoo in this case can be considered as a form of a soft biometric. In [66], the authors created a system to detect tattoos and de-identified it for privacy protection in still images. [PERSON] et al. [67] designed a system to distinguish between tattoed and non-tattoed areas using a deep convolutional neural network. The neural network grouped the patches into blobs and replaced the pixel color inside the tattoo blob with the surrounding skin color to de-identify it.