## 3 Comprehensive Classification of Existing De-identification Methods
Table 2 summarizes research focusing on _soft biometric preserving utility lost de-identification_ and Table 3 summarizes research focusing on _soft biometric preserving utility retained de-identification_. The k-Same technique is the commonly used method for soft biometrics preserving utility lost de-identification [16]. This method determines the similarity between faces based on a distance metric and creates new faces by averaging image components, which may be the original image pixels or eigenvectors, and is shown to be more effective than pixelation or blurring. [PERSON] et al. [34] proposed the k-Same-M approach for face de-identification while preserving facial expression as soft biometrics by incorporating the Active Appearance Model (AAM). An active appearance model is a computer vision algorithm for matching a statistical model of object shape and appearance to a new image. [PERSON] et al. [35] advanced this research further by proposing the k-Same-Net, which combined the k-anonymity algorithm with generative neural network architecture. Their method could de-identify the face while preserving its utility, natural appearance, and emotions. [PERSON] et al. [36] explicitly preserved race, gender, and age attributes in face de-identification. Given a test image, the authors computed the attributes and selected the corresponding attribute-specific AAMs. [PERSON] et al. [37] adopted a model-based approach, representing faces as AAM features to avoid ghosting artifacts. Their approach identified k faces that were furthest away from a given probe face image. The algorithm calculated the average of the k furthest faces and returned it as a de-identified face, keeping the facial expression unchanged. [PERSON] et al. [38] proposed a face de-identifying method using multi-mode discriminant analysis with AAM. By using orthogonal decomposition of the multi-attribute face image, they established the independent subspace of each attribute,  
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obtained the corresponding parameter, and selectively changed the parameters of other attributes in addition to the expression. Their system only preserves the facial expression and obscures all the other attributes. The k-Same furthest algorithm guarantees that the face de-identified by it can never be recognized as the original face, as long as the identity distance measure used to recognize the de-identified faces is the same as that used by the k-Same furthest method. Apart from the k-same based method, several other approaches were proposed for soft biometrics preserving de-identification. [PERSON] et al. [39] introduced an interesting idea of face-swapping, where a new face was blended with an original face and then lighting and contrast were adjusted to create a naturally looking de-identified face. The system preserved the body shape and pose of the person. [PERSON] and [PERSON] [40] used a neural style transfer method for face-to-face attribute transfer. The target was to preserve the consistency of non-identity attributes between the input and anonymized data while keeping the soft biometrics unaffected by this transfer. [PERSON] et al. [41] adopted a neural style transfer technique for face, hairstyle, and clothing de-identification, keeping the body shape preserved. Another work by the same authors focused on transferring a style content of an input image to the target image and performed a full-body and face obfuscation, while the shape of the subject remains identifiable [42]. [PERSON] et al. [43] proposed an identity concealing method that preserved the pose, hair color, and facial expression. Their method added an adversarial identity mask to the original face image to remove the identity. [PERSON] and [PERSON] [44] used Facial Identity Preserving (FIP) features to preserve the aesthesis of the original images, while still achieving k-anonymity-based facial image de-identification.