A digital forensic image verification system according to an embodiment of the present disclosure includes an imaging device having a imaging unit configured to produce an image, a first hash value generator configured to generate a first hash value for the image, and a transmitting unit configured to transmit the image and the first hash value; and an image storage device having a receiving unit configured to receive the image and the first hash value from the imaging device, a second hash value generator configured to generate a second hash value for the image, and a controller configured to compare the first hash value and the second hash value with each other.

Example implementations include a method, apparatus and computer-readable medium for person detection by a computer device, comprising receiving a plurality of video frames from a camera located in an environment, wherein the video frames include a depiction of a person in the environment. The implementations further include identifying attributes of the person from the video frames and comparing the attributes with attribute entries in a database of suspicious persons, wherein the database of suspicious persons includes attributes of a plurality of persons associated with an alert in the environment. The implementations include retrieving a profile indicative of an alert history of the suspicious person in response to determining a match between the attributes of the person and an entry in the database corresponding to a suspicious person, and transmitting, to a second computer device, the retrieved profile and a notification that indicates that the suspicious person is in the environment.

A method of collecting biological material with the help of a mobile terminal, the method including the following steps: acquiring a first image and extracting a first candidate biometric dataset therefrom; comparing the candidate biometric dataset with reference biometric datasets and determining similarity scores; comparing the similarity scores with a first predetermined threshold; and when none of the similarity scores is greater than the first predetermined threshold, proceeding to collect biological material from the first dermatoglyph.

Systems, methods, and kits are disclosed for collection, labeling and analyzing biological samples containing nucleic acid in conjunction with collecting at least one ridge and valley signature of an individual. Such devices and methods are used in forensic, human identification, access control and screening technologies to rapidly process an individual's identity or determine the identity of an individual.

Systems and methods for using a mobile device camera to capture photos of latent fingerprints are disclosed. Various embodiments disclosed implement machine learning and pattern matching algorithms to determine the quality of the captured photo of a latent fingerprint. The quality determined by the algorithms may be used to provide feedback to a user (e.g., a CSI) such that the user can capture higher quality images that improve the reliability in using the fingerprint for search and/or matching.

A method of determining footprint identity using a dimension reduction algorithm according to an embodiment includes: pre-processing to process three-dimensional (3D) image data about footprints of a first person and a second person and convert the 3D image data into one-dimensional (1D) data about the footprints of the first person and the second person; calculating a distribution of cross-correlation coefficients between two pieces of 1D data about footprints of the first person (SC: same footwear correlation) and a distribution of cross-correlation coefficients between the 1D data about the footprints of the first person and the second person (DC: difference footwear correlation); and calculating a likelihood ratio based on the SC and the DC to determine the degree of correspondence between the footprints of the first person and the second person.

A method of determining footprint identity using a dimension reduction algorithm according to an embodiment includes: pre-processing to process three-dimensional (3D) image data about footprints of a first person and a second person and convert the 3D image data into one-dimensional (1D) data about the footprints of the first person and the second person; calculating a distribution of cross-correlation coefficients between two pieces of 1D data about footprints of the first person (SC: same footwear correlation) and a distribution of cross-correlation coefficients between the 1D data about the footprints of the first person and the second person (DC: difference footwear correlation); and calculating a likelihood ratio based on the SC and the DC to determine the degree of correspondence between the footprints of the first person and the second person.

A server analyzes feature information including a whole body and a face of a person reflected in a video image sent from a monitoring camera and store a whole body image obtained by cutting out the whole body of the person and a face image obtained by cutting out the face of the person. In response to designation of a person of interest, the server executes first collation processing targeted for the whole body image of the person of interest and second collation processing targeted for the face image of the person of interest. Also, in response to identification of a person matching at least one of the whole body image and the face image of the person of interest by at least one of the first collation processing and the second collation processing, the server outputs a notification that the person of interest is found.

A method for retrieving footprint images is provided, comprising: pre-training models; cleaning footprint data and conducting expansion pre-processing by using the pre-trained models, dividing the footprint data into multiple data sets; adjusting full connection layers and classification layers of the models; training the models again by using the data sets through the parameters of the pre-trained models; saving the models trained twice, removing the classification layer, executing a feature extraction for images in an image library and a retrieval library to form a feature index library; connecting the features extracted by three models to form fused features, establishing a fused feature vector index library; extracting the features of the images in the image library to be retrieved in advance, and establishing a feature vector library; calculating distances in the retrieval library and the image library when a single footprint image is inputted, thereby outputting the image with the highest similarity.

A method for retrieving footprint images is provided, comprising: pre-training models; cleaning footprint data and conducting expansion pre-processing by using the pre-trained models, dividing the footprint data into multiple data sets; adjusting full connection layers and classification layers of the models; training the models again by using the data sets through the parameters of the pre-trained models; saving the models trained twice, removing the classification layer, executing a feature extraction for images in an image library and a retrieval library to form a feature index library; connecting the features extracted by three models to form fused features, establishing a fused feature vector index library; extracting the features of the images in the image library to be retrieved in advance, and establishing a feature vector library; calculating distances in the retrieval library and the image library when a single footprint image is inputted, thereby outputting the image with the highest similarity.