By Riad I. Hammoud, Besma R. Abidi, Mongi A. Abidi
This publication presents an considerable insurance of theoretical and experimental state of the art paintings in addition to new traits and instructions within the biometrics box. It bargains scholars and software program engineers a radical figuring out of ways a few middle low-level development blocks of a multi-biometric method are carried out. whereas this booklet covers various biometric features together with facial geometry, 3D ear shape, fingerprints, vein constitution, voice, and gait, its major emphasis is put on multi-sensory and multi-modal face biometrics algorithms and platforms. "Multi-sensory" refers to combining information from or extra biometric sensors, similar to synchronized reflectance-based and temperature-based face photographs. "Multi-modal" biometrics potential fusing or extra biometric modalities, like face photographs and voice trees. This useful reference includes 4 designated elements and a quick advent bankruptcy. the 1st half addresses new and rising face biometrics. Emphasis is put on biometric platforms the place unmarried sensor and unmarried modality are hired in hard imaging stipulations. the second one half on multi-sensory face biometrics bargains with the private identity activity in not easy variable illuminations and outside working eventualities by means of making use of noticeable and thermal sensors. The 3rd a part of the publication specializes in multi-modal face biometrics by means of integrating voice, ear, and gait modalities with facial information. The final half provides known chapters on multi-biometrics fusion methodologies and function prediction thoughts.
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Extra resources for Face Biometrics for Personal Identification: Multi-Sensory Multi-Modal Systems (Signals and Communication Technology)
Example text
3. Database of passport images age difference (years) no. of image pairs 1–2 3–4 5–7 8–9 10 years 4 years 1 years 2 years 165 104 81 115 6 years 4 years 5 years 1 years Fig. 2. A few sample age separated images of individuals retrieved from their passports We wish to address the following problems: how similar are a pair of age separated face images of an individual? How do inherent changes in a human face due to aging effects affect facial similarity? Given a pair of age separated face images of an individual, what is the confidence measure associated with verifying the identity?
4 m. However, their camera zoom remains low and constant. In comparison, our database 44 Y. Yao et al. 1. Designation of magnification/distance ranges range magnification (×) distance (m) low/short medium high/long extreme 1–3 <3 3–10 3–10 10–30 10–100 >30 >100 aims at high to extreme magnifications and long to extreme distances. For indoor sequences, high magnifications (10× to 20×) are used while for outdoor sequences extreme magnifications are obtained with a maximum of 375×. As a result, degradations induced by high magnification and long distance, such as magnification blur, are systematically present in the data.
M Next, from a set of extrapersonal image differences {zi }i=1 ∈ ΩE , we estimate the likelihood function for the data P (zi | ΩE ). Adopting a similar approach as earlier, the extrapersonal space is decomposed into two complementary spaces: the feature space and the error space. Since the assumption of Gaussian distribution of extrapersonal image differences may not hold, we adopt a parametric mixture model (mixture of Gaussian) to estimate the marginal density in the feature space and follow a similar approach as earlier to estimate the marginal density in the orthogonal complement space.