Abstract: A set of training vectors may be identified. Each training vector may be mapped to either a male gender or a female gender, and each training vector may represent facial landmarks derived from a respective facial image. An input vector of facial landmarks may also be identified. The facial landmarks of the input vector may be derived from a particular facial image. A feature vector may containing a subset of the facial landmarks may be selected from the input vector. A weighted comparison may be performed between the feature vector and each of the training vectors. Based on a result of the weighted comparison, the particular facial image may be classified as either the male gender or the female gender.

Abstract: A facial image may be annotated with the plurality of facial landmarks. These facial landmarks may be points or regions of the face that are indicative, either alone or in combination with other facial landmarks, of at least one demographic characteristic. Demographic characteristics include, for example, age, race, and/or gender. Based on the demographic characteristic being analyzed, one or more of these facial landmarks may be selected and arranged into an input vector. Then, the input vector may be compared to one or more of the training vectors. An outcome of this comparison may involve in the given facial image being classified into a category germane to the analyzed demographic characteristic (e.g., an age range or age, a racial category, and/or a gender).

Abstract: A set of training vectors may be identified. Each training vector may be mapped to either a male gender or a female gender, and each training vector may represent facial landmarks derived from a respective facial image. An input vector of facial landmarks may also be identified. The facial landmarks of the input vector may be derived from a particular facial image. A feature vector may containing a subset of the facial landmarks may be selected from the input vector. A weighted comparison may be performed between the feature vector and each of the training vectors. Based on a result of the weighted comparison, the particular facial image may be classified as either the male gender or the female gender.

Abstract: A set of training vectors may be identified. Each training vector may be mapped to either a male gender or a female gender, and each training vector may represent facial landmarks derived from a respective facial image. An input vector of facial landmarks may also be identified. The facial landmarks of the input vector may be derived from a particular facial image. A feature vector may containing a subset of the facial landmarks may be selected from the input vector. A weighted comparison may be performed between the feature vector and each of the training vectors. Based on a result of the weighted comparison, the particular facial image may be classified as either the male gender or the female gender.

Abstract: A facial image may be annotated with the plurality of facial landmarks. These facial landmarks may be points or regions of the face that are indicative, either alone or in combination with other facial landmarks, of at least one demographic characteristic. Demographic characteristics include, for example, age, race, and/or gender. Based on the demographic characteristic being analyzed, one or more of these facial landmarks may be selected and arranged into an input vector. Then, the input vector may be compared to one or more of the training vectors. An outcome of this comparison may involve in the given facial image being classified into a category germane to the analyzed demographic characteristic (e.g., an age range or age, a racial category, and/or a gender).

Abstract: A facial image may be annotated with the plurality of facial landmarks. These facial landmarks may be points or regions of the face that are indicative, either alone or in combination with other facial landmarks, of at least one demographic characteristic. Demographic characteristics include, for example, age, race, and/or gender. Based on the demographic characteristic being analyzed, one or more of these facial landmarks may be selected and arranged into an input vector. Then, the input vector may be compared to one or more of the training vectors. An outcome of this comparison may involve in the given facial image being classified into a category germane to the analyzed demographic characteristic (e.g., an age range or age, a racial category, and/or a gender).

Abstract: A set of training vectors may be identified. Each training vector may be mapped to either a male gender or a female gender, and each training vector may represent facial landmarks derived from a respective facial image. An input vector of facial landmarks may also be identified. The facial landmarks of the input vector may be derived from a particular facial image. A feature vector may containing a subset of the facial landmarks may be selected from the input vector. A weighted comparison may be performed between the feature vector and each of the training vectors. Based on a result of the weighted comparison, the particular facial image may be classified as either the male gender or the female gender.