Matching Facial Composite Sketches to Police Mug-Shot Images Based on Geometric Features

Singh, Drishtant; Gupta, Rajat

doi:10.9790/0661-16372935

Cited by 3 publications

(2 citation statements)

References 15 publications

(11 reference statements)

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“…In this process, instead of using the entire face region, we propose to use only the 60 percent from the top of the face region to detect the eye pairs. Once detected, the geometric model [16] can be used to separate the region of right eye and left eye. We apply the geometrical method to extract the right eye and left eye regions due to the fact that both eyes have symmetrical geometrical properties.…”

Section: Pre-processingmentioning

confidence: 99%

Efficient Iris and Eyelids Detection from Facial Sketch Images

Boonchuan

Setumin

Radman

et al. 2018

ELCVIA

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In this paper, we propose a simple yet effective technique for an automatic iris and eyelids detection method for facial sketch images. Our system uses Circular Hough Transformation (CHT) algorithm for iris localization process and a low level grayscale analysis for eyelids contour segmentation procedure. We limit the input face for the system to facial sketch photos with frontal pose, illumination invariant, neutral expression and without occlusions. CUHK and IIIT-D sketch databases are used to acquire the experimental results. As to validate the proposed algorithm, experiments on ground truth for iris and eyelids segmentation, which are prepared at our lab, is conducted. The iris segmentation from the proposed method gives the best accuracy of 92.93 and 86.71 based on F-measure evaluation for IIIT-D and CUHK, respectively. For eyelids segmentation, on the other hand, the proposed algorithm achieves an average of 4 standard deviation which indicates the closeness of proposed method to ground truth.

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Section: Pre-processingmentioning

confidence: 99%

Efficient Iris and Eyelids Detection from Facial Sketch Images

Boonchuan

Setumin

Radman

et al. 2018

ELCVIA

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“…A small number of extensively used software kits to produced computerized sketches contains IdentiKit, Mac-a-Mug, and FACES. This software allows synthesizing sketches by selecting a sketch by choosing a set of facial component, examples of eye, hair, eyebrow, mouth, shape, nose, and eyeglass [12].CS created by viewing the mug shot will not be much useful in criminal investigations. Surveillance composites can also be created by viewing the image of the mug-shot these images are of having low quality.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Technique for Face Sketch Recognition Using DCNN

Patil,

2019

IJITEE

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Face Recognition (FR) is considered as one of the chief use in the investigation of criminals. In the majority of the cases, information about the criminal is not available. In such situations, sketch artist draw the sketch of the guess with the oral explanation provided by the eyewitness. These sketches can then be matched manually against mug shot photos. This process is time-consuming. Hence there require a method that efficiently goes with composite sketches to the gallery of mug shot databases. Thus the proposed system uses a scheme for matching composite sketch and photo images, photo image features are extracted and fused to train the system. Composite Sketch feature is matched with face photo images. Feature extraction (FE) is done using Multi-Scale Local Binary Patterns (MLBP) Tchebichef Moments and Multiscale Circular Weber Local Descriptor (MCWLD), Principal Component Analysis (PCA) is used for fusion of extracted features, DCNN used as a classifier to recognize the face. The experiments are conducted using PRIP-HDC dataset and the proposed system gives good accuracy in face recognition.

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Face Sketch Recognition Using Computer Vision

Meghana¹

2021

IJRASET

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Now-a-days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each-other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However, studies say that hand-drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victim’s sketches on behalf of the description provided by an eyewitness. Sometimes suspect uses special mask to hide some common features of faces like nose, eyes, lips, face-color etc. but the outliner features of face biometrics one could never hide. Here we concentrate on some specific facial geometric feature which could be used to calculate some ratio of similarities from the template photograph database against the forensic sketches. The project describes the design of a system for face sketch recognition by a computer vision approach like Discrete Cosine Transform (DCT), Local Binary Pattern Histogram (LBPH) algorithm and a supervised machine learning model called Support Vector Machine (SVM) for face recognition. Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

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Matching Facial Composite Sketches to Police Mug-Shot Images Based on Geometric Features

Cited by 3 publications

References 15 publications

Efficient Iris and Eyelids Detection from Facial Sketch Images

Efficient Iris and Eyelids Detection from Facial Sketch Images

An Integrated Technique for Face Sketch Recognition Using DCNN

Face Sketch Recognition Using Computer Vision

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